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    <title>Pinboard (cshalizi)</title>
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    <description>recent bookmarks from cshalizi</description>
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	<rdf:li rdf:resource="https://hsm.stackexchange.com/questions/6092/when-did-error-propagation-become-prominent-in-physics/12207#12207"/>
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	<rdf:li rdf:resource="https://doi.org/10.1093/oso/9780197504000.001.0001"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2004.06425"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2012.00174"/>
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	<rdf:li rdf:resource="https://www.jstor.org/stable/10.5749/j.cttttcnc"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1908.06346"/>
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	<rdf:li rdf:resource="https://www.propublica.org/nerds/item/infographics-in-the-time-of-cholera"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/0808.4032"/>
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	<rdf:li rdf:resource="http://mpra.ub.uni-muenchen.de/34117/"/>
	<rdf:li rdf:resource="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=272888"/>
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	<rdf:li rdf:resource="http://arxiv.org/abs/1003.0188"/>
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  </channel><item rdf:about="https://www.journals.uchicago.edu/doi/full/10.1086/733983">
    <title>The (Local) Rise and (Global) Fall of the “Coefficient of Racial Likeness” | Isis: Vol 116, No 1</title>
    <dc:date>2025-06-16T22:27:42+00:00</dc:date>
    <link>https://www.journals.uchicago.edu/doi/full/10.1086/733983</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The “Coefficient of Racial Likeness” (CRL) ascribed to Karl Pearson (1857–1936) was born in the Biometric Laboratory in London. It was developed with the purpose to determine the distance between two samples of different origins. Widely used but also distrusted before being ultimately replaced by a true statistical measure of divergence, the Mahalanobis distance (D2), the global biography of the CRL reveals the social, scientific, and historical forces at play that determined the lifespan of the coefficient, its success and fall from grace. Closely linked to Pearson personally, he could no longer control its fate once the CRL had escaped his laboratory and gotten into the hands of critical mathematicians, or to China where results of racial classification based on a single number and the assumption of homogeneity did not please the audience."

--- This statistic, which I'd never heard of, seems to be (basically) a variance-weighted sum (over coordinates) of squared differences in group means, as opposed to Mahalanobis's weighting by the inverse of the full covariance matrix.  And it's interesting that M. seems to have been wanting to improve on this.]]></description>
<dc:subject>to:NB history_of_statistics have_skimmed</dc:subject>
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<item rdf:about="https://www.degruyter.com/document/isbn/9781503642102/html">
    <title>Thinking Through Data: How Outliers, Aggregates, and Patterns Shape Perception</title>
    <dc:date>2025-03-14T17:49:16+00:00</dc:date>
    <link>https://www.degruyter.com/document/isbn/9781503642102/html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We encounter digital data processing on a range of platforms and in a multitude of contexts today: in the predictive algorithms of the financial sector, in drones, insurance, and risk management, in smart cities, biometrics, medicine, and more. This fascinating book explores the historical context of the current data-driven paradigm and explains how elusive yet crucial statistical concepts such as outliers, aggregates, and patterns form how we sense and make sense of data. From the sixteenth century's embodied measurements of the foot, through the blurred facial features of L'Homme Moyen, to the image aggregates of today's security systems, the examples collected in this book illustrate the central role of aesthetics throughout the history of statistical knowledge production. Taking its point of departure in analyses and discussions of contemporary artistic experiments by Rossella Biscotti, Stéphanie Solinas, and Adam Broomberg and Oliver Chanarin, the book broadens our understanding of the structures of knowledge and methods in statistical computation beyond optimistic narratives of calculative power. Venturing out into the tails of the distributions—to the systemically overlooked and excluded—this book challenges us to embrace an alternative view of modern data processing."

--- Sounds like "baby stats. for the Theory-brained, via pictures", but I actually think that would be amazing so I hope that's what this is and it's good. 
]]></description>
<dc:subject>to:NB books:noted statistics aesthetics history_of_statistics visual_display_of_quantitative_information downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:adb95a135c62/</dc:identifier>
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<item rdf:about="https://press.uchicago.edu/ucp/books/book/chicago/M/bo114655831">
    <title>The Matter of Black Living: The Aesthetic Experiment of Racial Data, 1880–1930, Womack</title>
    <dc:date>2022-05-11T18:30:49+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/chicago/M/bo114655831</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As the nineteenth century came to a close and questions concerning the future of African American life reached a fever pitch, many social scientists and reformers approached post-emancipation Black life as an empirical problem that could be systematically solved with the help of new technologies like the social survey, photography, and film. What ensued was nothing other than a “racial data revolution,” one which rendered African American life an inanimate object of inquiry in the name of social order and racial regulation. At the very same time, African American cultural producers and intellectuals such as W. E. B. Du Bois, Kelly Miller, Sutton Griggs, and Zora Neale Hurston staged their own kind of revolution, un-disciplining racial data in ways that captured the dynamism of Black social life.
"The Matter of Black Living excavates the dynamic interplay between racial data and Black aesthetic production that shaped late nineteenth-century social, cultural, and literary atmosphere. Through assembling previously overlooked archives and seemingly familiar texts, Womack shows how these artists and writers recalibrated the relationship between data and Black life. The result is a fresh and nuanced take on the history of documenting Blackness. The Matter of Black Living charts a new genealogy from which we can rethink the political and aesthetic work of racial data, a task that has never been more urgent."

--- How was Du Bois not 100% disciplinary sociology & political economy?!?]]></description>
<dc:subject>to:NB books:noted history_of_statistics history_of_science american_history the_american_dilemma downloaded</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:bd2d69505237/</dc:identifier>
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<item rdf:about="https://philpapers.org/rec/KNOTAI-3">
    <title>Joshua Knobe &amp; Henry Cowles, The Average Isn’t Normal - PhilPapers</title>
    <dc:date>2022-01-19T16:32:56+00:00</dc:date>
    <link>https://philpapers.org/rec/KNOTAI-3</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Within contemporary science, it is common practice to compare data points to the _average_, i.e., to the statistical mean. Because this practice is so familiar, it might at first appear not to be the sort of thing that requires explanation. But recent research in cognitive science gives us reason to adopt the opposite perspective. Research on the cognitive processes involved in people’s ordinary efforts to make sense of the world suggests that, instead of using a purely statistical notion of the average, people tend to use a value-laden notion of the _normal_. This finding about ordinary cognition gives us reason to rethink certain familiar facts about scientific practice. In particular, it suggests that the fact that scientists so often make use of the statistical average should be seen as a highly surprising fact, the sort of thing that calls out for explanation. To understand it, we turn to work in the history of science, and especially to work on the ways in which the practice of science changed over the course of the 19th century."]]></description>
<dc:subject>to:NB statistics history_of_science history_of_statistics philosophy_of_science via:?</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:47e283348fed/</dc:identifier>
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<item rdf:about="https://doi.org/10.1214/20-STS798">
    <title>Khinchin’s 1929 Paper on Von Mises’ Frequency Theory of Probability</title>
    <dc:date>2021-08-05T02:53:17+00:00</dc:date>
    <link>https://doi.org/10.1214/20-STS798</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In 1929, a few years prior to his colleague Kolmogorov’s Grundbegriffe, the leading Russian probabilist Khinchin published a paper in which he commented on the foundational ambitions of von Mises’ frequency theory of probability. This brief introduction provides background and context for the English translation of Khinchin’s historically revealing paper, published as an online supplement."]]></description>
<dc:subject>to:NB history_of_statistics history_of_mathematics probability</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:64593e8f8883/</dc:identifier>
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<item rdf:about="https://journals.sagepub.com/doi/full/10.1177/0306312718821726">
    <title>The social life of data points: Antecedents of digital technologies - David Armstrong, 2019</title>
    <dc:date>2021-07-16T04:26:50+00:00</dc:date>
    <link>https://journals.sagepub.com/doi/full/10.1177/0306312718821726</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Recent technological advances such as microprocessors and random-access memory have had a significant role in gathering, storing and processing digital data, but the basic principles underpinning such data management were established in the century preceding the digital revolution. This paper maps the emergence of those older technologies to show that the logic and imperative for the surveillance potential of more recent digital technologies was laid down in a pre-digital age. The paper focuses on the development of the data point from its use in punch cards in the late 19th century through its manipulation in ideas about correlation to its collection via self-completion questionnaires. Some ways in which medicine and psychology have taken up and deployed the technology of data points are used as illustrative exemplars. The paper concludes with a discussion of the role of data points in defining human identity."]]></description>
<dc:subject>to:NB history_of_science history_of_statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8e31b99828e6/</dc:identifier>
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<item rdf:about="https://www.radcliffe.harvard.edu/news-and-ideas/flash-of-genius">
    <title>Flash of Genius | Radcliffe Institute for Advanced Study at Harvard University</title>
    <dc:date>2021-06-04T03:58:21+00:00</dc:date>
    <link>https://www.radcliffe.harvard.edu/news-and-ideas/flash-of-genius</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>history_of_science history_of_mathematics history_of_statistics monte_carlo to_teach:statcomp history_of_computing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1cf9aa9b6688/</dc:identifier>
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<item rdf:about="https://www.sciencedirect.com/science/article/pii/S0047259X02000210">
    <title>Results in statistical discriminant analysis: a review of the former Soviet Union literature - ScienceDirect</title>
    <dc:date>2021-03-10T15:49:35+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S0047259X02000210</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[This looks very interesting, and makes me want to go back to the notes/materials from my first pattern recognition/ML course as a graduate student at Wisconsin, taught by a Russian mechanical engineering professor (V. J. Lumelsky, Ph.D. 1970, Moscow Institute of Control Sciences).]]></description>
<dc:subject>to:NB history_of_statistics ussr via:rvenkat classifiers learning_theory to_teach:childs_garden_of_statistical_learning_theory re:paradigm_formation_in_statistical_learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
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<item rdf:about="https://hsm.stackexchange.com/questions/6092/when-did-error-propagation-become-prominent-in-physics/12207#12207">
    <title>statistics - When did error propagation become prominent in physics? - History of Science and Mathematics Stack Exchange</title>
    <dc:date>2021-02-19T06:13:04+00:00</dc:date>
    <link>https://hsm.stackexchange.com/questions/6092/when-did-error-propagation-become-prominent-in-physics/12207#12207</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Your question is about when error propagation become prominent (rather than about when it first appeared). That part I can answer by saying that Raymond Birge in 1939 said that the question of how to assign an uncertainty has been discussed for decades but the subject matter of error propagation is one for which "many scientists still fail to avail themselves" and that "others frequently use the theory [of error propagation] incorrectly and thus arrive at quite misleading conclusions"."

--- I presume this explains why it was drilled into us in physics lab class (though I can't now remember if the actual lab rooms were in Birge Hall or Le Conte).]]></description>
<dc:subject>propagation_of_error history_of_statistics history_of_physics to_teach track_down_references</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f256820eaa83/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_physics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
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</item>
<item rdf:about="https://hsm.stackexchange.com/questions/12200/when-was-the-law-of-propagation-of-error-first-stated">
    <title>mathematics - When was the &quot;Law of Propagation of Error&quot; first stated? - History of Science and Mathematics Stack Exchange</title>
    <dc:date>2021-02-19T06:09:01+00:00</dc:date>
    <link>https://hsm.stackexchange.com/questions/12200/when-was-the-law-of-propagation-of-error-first-stated</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["That formula was stated (albeit in a rather different notation) and derived in section 149 of Galloway (1839, A treatise on probability, Adam and Charles Black), of which Google Books has the full text available. That work appears to be a republication as a book of an article from the 7th edition of Encyclopaedia Britannica, which was published in 1827.
"I can't be sure that that's the earliest appearance of it, but as I argued in my answer to the above-mentioned related question, it can't have appeared very much earlier, because the formula relies on a conceptual understanding of errors that was first clearly described in 1798, and an approximation method for integrals which was invented in 1774."]]></description>
<dc:subject>propagation_of_error history_of_statistics track_down_references to_teach</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5df8520ea32e/</dc:identifier>
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<item rdf:about="https://doi.org/10.1093/oso/9780197504000.001.0001">
    <title>Calculating Race: Racial Discrimination in Risk Assessment - Oxford Scholarship</title>
    <dc:date>2021-01-16T04:25:18+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780197504000.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Calculating Race: Racial Discrimination in Risk Assessment presents the historical relationship between statistical risk assessment and race in the United States. It illustrates how, through a reliance on the variable of race, actuarial science transformed the nature of racism and, in turn, helped usher racial disparities in wealth, incarceration, and housing from the nineteenth century into the twentieth. The monograph begins by investigating the development of statistical risk assessment explicitly based on race in the late-nineteenth-century life insurance industry. It then traces how such risk assessment migrated from industry to government, becoming a guiding force in parole decisions and in federal housing policy. Finally, it concludes with an analysis of “proxies” for race—statistical variables that correlate significantly with race—in order to demonstrate the persistent presence of race in risk assessment even after the anti-discrimination regulations won by the Civil Rights Movement. Offering readers a new perspective on the historical importance of actuarial science in structural racism, Calculating Race is a particularly timely contribution as Big Data and algorithmic decision-making increasingly pervade American life."

--- Next-to-last tag because I am _very_ doubtful about the direction of the causal arrows sketched in the book summary here.]]></description>
<dc:subject>books:noted racism algorithmic_fairness history_of_statistics the_american_dilemma to_read to_teach:statistics_of_inequality_and_discrimination color_me_skeptical downloaded in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2f2bb55902af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_american_dilemma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.06425">
    <title>[2004.06425] Computing Bayes: Bayesian Computation from 1763 to the 21st Century</title>
    <dc:date>2020-12-14T14:57:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.06425</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characterize Bayesian analysis, with the rules of probability used to transform prior probability distributions for all unknowns - parameters, latent variables, models - into posterior distributions, subsequent to the observation of data. Conducting Bayesian analysis requires the evaluation of integrals in which these probability distributions appear. Bayesian computation is all about evaluating such integrals in the typical case where no analytical solution exists. This paper takes the reader on a chronological tour of Bayesian computation over the past two and a half centuries. Beginning with the one-dimensional integral first confronted by Bayes in 1763, through to recent problems in which the unknowns number in the millions, we place all computational problems into a common framework, and describe all computational methods using a common notation. The aim is to help new researchers in particular - and more generally those interested in adopting a Bayesian approach to empirical work - make sense of the plethora of computational techniques that are now on offer; understand when and why different methods are useful; and see the links that do exist, between them all."]]></description>
<dc:subject>to:NB bayesianism history_of_statistics computational_statistics robert.christian_p. to_read to_teach:statcomp</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:07e2869f2a9c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robert.christian_p."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statcomp"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.00174">
    <title>[2012.00174] What are the most important statistical ideas of the past 50 years?</title>
    <dc:date>2020-12-02T15:23:54+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.00174</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We argue that the most important statistical ideas of the past half century are: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss common features of these ideas, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this article is to provoke thought and discussion regarding the larger themes of research in statistics and data science."]]></description>
<dc:subject>to:NB history_of_statistics gelman.andrew</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:68649cf80c6a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:gelman.andrew"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.princeton.edu/books/paperback/9780691208428/the-rise-of-statistical-thinking-1820-1900">
    <title>The Rise of Statistical Thinking, 1820–1900 | Princeton University Press</title>
    <dc:date>2020-07-07T14:32:47+00:00</dc:date>
    <link>https://press.princeton.edu/books/paperback/9780691208428/the-rise-of-statistical-thinking-1820-1900</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Not really a new edition, but:

"A new preface by the author looks at how the book has remained relevant since its initial publication, and considers the current place of statistics in scientific research."]]></description>
<dc:subject>books:recommended history_of_science history_of_statistics statistics 19th_century_history</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c6318650ead7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:recommended"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:19th_century_history"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/10.5749/j.cttttcnc">
    <title>Thicker than Blood: How Racial Statistics Lie on JSTOR</title>
    <dc:date>2019-08-25T20:55:42+00:00</dc:date>
    <link>https://www.jstor.org/stable/10.5749/j.cttttcnc</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read downloaded history_of_statistics eugenics racism statistics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dbb5a3f9309b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:eugenics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06346">
    <title>[1908.06346] Karl Pearson and the Logic of Science: Renouncing Causal Understanding (the Bride) and Inverted Spinozism</title>
    <dc:date>2019-08-20T14:16:01+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06346</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Karl Pearson is the leading figure of XX century statistics. He and his co-workers crafted the core of the theory, methods and language of frequentist or classical statistics -- the prevalent inductive logic of contemporary science. However, before working in statistics, K.Pearson had other interests in life, namely, in this order, philosophy, physics, and biological heredity. Key concepts of his philosophical and epistemological system of anti-Spinozism (a form of transcendental idealism) are carried over to his subsequent works on the logic of scientific discovery. This article's main goal is to analyze K.Pearson early philosophical and theological ideas and to investigate how the same ideas came to influence contemporary science, either directly or indirectly -- by the use of variant theories, methods and dialects of statistics, corresponding to variant statistical inference procedures and their specific belief calculi."]]></description>
<dc:subject>to:NB history_of_ideas history_of_science history_of_statistics lives_of_the_scientists pearson.karl</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0fee900ef3d4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lives_of_the_scientists"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearson.karl"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.uchicago.edu/ucp/books/book/chicago/C/bo38870755">
    <title>Collecting Experiments: Making Big Data Biology, Strasser</title>
    <dc:date>2019-08-03T22:29:03+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/chicago/C/bo38870755</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Databases have revolutionized nearly every aspect of our lives. Information of all sorts is being collected on a massive scale, from Google to Facebook and well beyond. But as the amount of information in databases explodes, we are forced to reassess our ideas about what knowledge is, how it is produced, to whom it belongs, and who can be credited for producing it.
"Every scientist working today draws on databases to produce scientific knowledge. Databases have become more common than microscopes, voltmeters, and test tubes, and the increasing amount of data has led to major changes in research practices and profound reflections on the proper professional roles of data producers, collectors, curators, and analysts.
"Collecting Experiments traces the development and use of data collections, especially in the experimental life sciences, from the early twentieth century to the present. It shows that the current revolution is best understood as the coming together of two older ways of knowing—collecting and experimenting, the museum and the laboratory. Ultimately, Bruno J. Strasser argues that by serving as knowledge repositories, as well as indispensable tools for producing new knowledge, these databases function as digital museums for the twenty-first century."]]></description>
<dc:subject>to:NB books:noted history_of_science history_of_statistics books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:135fde282a0d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.uchicago.edu/ucp/books/book/chicago/H/bo38181810">
    <title>How We Became Our Data: A Genealogy of the Informational Person, Koopman</title>
    <dc:date>2019-08-03T22:26:21+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/chicago/H/bo38181810</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We are now acutely aware, as if all of the sudden, that data matters enormously to how we live. How did information come to be so integral to what we can do? How did we become people who effortlessly present our lives in social media profiles and who are meticulously recorded in state surveillance dossiers and online marketing databases? What is the story behind data coming to matter so much to who we are?
"In How We Became Our Data, Colin Koopman excavates early moments of our rapidly accelerating data-tracking technologies and their consequences for how we think of and express our selfhood today. Koopman explores the emergence of mass-scale record keeping systems like birth certificates and social security numbers, as well as new data techniques for categorizing personality traits, measuring intelligence, and even racializing subjects. This all culminates in what Koopman calls the “informational person” and the “informational power” we are now subject to. The recent explosion of digital technologies that are turning us into a series of algorithmic data points is shown to have a deeper and more turbulent past than we commonly think. Blending philosophy, history, political theory, and media theory in conversation with thinkers like Michel Foucault, Jürgen Habermas, and Friedrich Kittler, Koopman presents an illuminating perspective on how we have come to think of our personhood—and how we can resist its erosion."]]></description>
<dc:subject>to:NB books:noted history_of_statistics history_of_ideas history_of_technology color_me_skeptical books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d939d0a5d7b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:suggest_to_library"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://global.oup.com/academic/product/matherons-theory-of-regionalised-variables-9780198835660?cc=us&amp;lang=en#">
    <title>Matheron's Theory of Regionalised Variables - Georges Matheron - Oxford University Press</title>
    <dc:date>2019-06-14T12:10:39+00:00</dc:date>
    <link>https://global.oup.com/academic/product/matherons-theory-of-regionalised-variables-9780198835660?cc=us&amp;lang=en#</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["n the summer of 1970, Georges Matheron, the father of geostatistics, presented a series of lectures at the Centre de Morphologie Mathmatique in France. These lectures would go on to become Matheron's Theory of Regionalized Variables, a seminal work that would inspire hundreds of papers and become the bedrock of numerous theses and books on the topic; however, despite their importance, the notes were never formally published. 
"In this volume, Matheron's influential work is presented as a published book for the first time. Originally translated into English by Charles Huijbregts, and carefully curated here, this book stays faithful to Matheron's original notes. The text has been ordered with a common structure, and equations and figures have been redrawn and numbered sequentially for ease of reference.
"While not containing any mathematical technicalities or case studies, the reader is invited to wonder about the physical meaning of the notions Matheron deals with. When Matheron wrote them, he considered the theory of linear geostatistics complete and the book his final one on the subject; however, this end for Matheron has been the starting point for most geostatisticians."]]></description>
<dc:subject>books:noted spatial_statistics history_of_statistics statistics to_teach:data_over_space_and_time in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6a8eb7f6f760/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/books/body-populace">
    <title>The Body Populace | The MIT Press</title>
    <dc:date>2018-12-19T18:25:02+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/body-populace</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In pre–World War I Europe, individual fitness was increasingly related to building and preserving collective society. Army recruitment offered the most important opportunity to screen male citizens' fitness, raising questions of how to define fitness for soldiers and how to translate this criteria outside the military context. In this book, Heinrich Hartmann explores the historical circumstances that shaped collective understandings of fitness in Europe before World War I and how these were intertwined with a fear of demographic decline and degeneration. This dynamic gained momentum through the circulation of knowledge among European nations, but also through the scenarios of military confrontations.
"Hartmann provides a science history of military statistics in Germany, France, and Switzerland in the decades preceding World War I, considering how information gathered during national conscriptions generated data about the health and fitness of the population. Defined by masculine concepts, conscription examinations went far beyond the individuals they tested and measured. Scholars of the time aspired to pin down the “nation” in concrete numerical terms, drawing on data from examinations to redefine society as a “collective body” that could be counted, measured, and examined. The Body Populace explores the historical specificity and contingency of data-gathering techniques, recounts their uses and abuses, and provides a timely contribution to the growing historiography of Big Data. It sheds light on a crucial moment in nineteenth and early twentieth century European history—when statistical data and demographical knowledge shaped new notions of masculinity, fostered fears of degeneration, and gave rise to eugenic thinking."]]></description>
<dc:subject>to:NB books:noted history_of_statistics history_of_ideas</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:763c0e2805ab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.propublica.org/nerds/item/infographics-in-the-time-of-cholera">
    <title>Infographics in the Time of Cholera - ProPublica</title>
    <dc:date>2016-03-17T17:04:25+00:00</dc:date>
    <link>https://www.propublica.org/nerds/item/infographics-in-the-time-of-cholera</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>history_of_statistics visual_display_of_quantitative_information the_present_before_it_was_widely_distributed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14badc2b698d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:visual_display_of_quantitative_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.uchicago.edu/ucp/books/book/chicago/H/bo20298894">
    <title>How Our Days Became Numbered: Risk and the Rise of the Statistical Individual, Bouk</title>
    <dc:date>2015-06-08T02:46:37+00:00</dc:date>
    <link>http://press.uchicago.edu/ucp/books/book/chicago/H/bo20298894</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Long before the age of "Big Data" or the rise of today's "self-quantifiers," American capitalism embraced "risk"--and proceeded to number our days. Life insurers led the way, developing numerical practices for measuring individuals and groups, predicting their fates, and intervening in their futures. Emanating from the gilded boardrooms of Lower Manhattan and making their way into drawing rooms and tenement apartments across the nation, these practices soon came to change the futures they purported to divine."]]></description>
<dc:subject>books:noted history_of_ideas history_of_statistics statistics insurance economics the_present_before_it_was_widely_distributed risk_assessment in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ca49e51b8790/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:insurance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_present_before_it_was_widely_distributed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:risk_assessment"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://global.oup.com/academic/product/a-history-of-econometrics-9780199679348?cc=us&amp;lang=en&amp;tab=description">
    <title>A History of Econometrics: The Reformation from the 1970s - Hardcover - Duo Qin - Oxford University Press</title>
    <dc:date>2013-09-18T00:36:33+00:00</dc:date>
    <link>http://global.oup.com/academic/product/a-history-of-econometrics-9780199679348?cc=us&amp;lang=en&amp;tab=description</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Reformation of Econometrics is a sequel to The Formation of Econometrics: A Historical Perspective (1993, OUP) which traces the formation of econometric theory during the period 1930-1960. This book provides an account of the advances in the field of econometrics since the 1970s. Based on original research, it focuses on the reformists' movement and schools of thought and practices that attempted a paradigm shift in econometrics in the 1970s and 1980s. 
"It describes the formation and consolidation of the Cowles Commission (CC) paradigm and traces and analyses the three major methodological attempts to resolve problems involved in model choice and specification of the CC paradigm. These attempts have reoriented the focus of econometric research from internal questions (how to optimally estimate a priori given structural parameters) to external questions (how to choose, design, and specify models). It also examines various modelling issues and problems through two case studies - modelling the Phillips curve and business cycles. The third part of the book delves into the development of three key aspects of model specification in detail - structural parameters, error terms, and model selection and design procedures. The final chapter uses citation analyses to study the impact of the CC paradigm over the span of three and half decades (1970-2005). The citation statistics show that the impact has remained extensive and relatively strong in spite of certain weakening signs. It implies that the reformative attempts have fallen short of causing a paradigm shift."]]></description>
<dc:subject>to:NB history_of_economics history_of_statistics statistics econometrics history_of_science books:noted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f46641e2f9ae/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bj/1377612845">
    <title>Seneta : A Tricentenary history of the Law of Large Numbers</title>
    <dc:date>2013-09-04T21:05:36+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bj/1377612845</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The Weak Law of Large Numbers is traced chronologically from its inception as Jacob Bernoulli’s Theorem in 1713, through De Moivre’s Theorem, to ultimate forms due to Uspensky and Khinchin in the 1930s, and beyond. Both aspects of Jacob Bernoulli’s Theorem: 1. As limit theorem (sample size n→∞), and: 2. Determining sufficiently large sample size for specified precision, for known and also unknown p (the inversion problem), are studied, in frequentist and Bayesian settings. The Bienaymé–Chebyshev Inequality is shown to be a meeting point of the French and Russian directions in the history. Particular emphasis is given to less well-known aspects especially of the Russian direction, with the work of Chebyshev, Markov (the organizer of Bicentennial celebrations), and S.N. Bernstein as focal points."]]></description>
<dc:subject>to:NB probability history_of_statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f5a498d7ed5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mitpress.mit.edu/books/raw-data-oxymoron">
    <title>&quot;Raw Data&quot; Is an Oxymoron | The MIT Press</title>
    <dc:date>2013-06-26T16:22:24+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/raw-data-oxymoron</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We live in the era of Big Data, with storage and transmission capacity measured not just in terabytes but in petabytes (where peta- denotes a quadrillion, or a thousand trillion). Data collection is constant and even insidious, with every click and every “like” stored somewhere for something. This book reminds us that data is anything but “raw,” that we shouldn’t think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book’s essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously “cooked” in the processes of their collection and use; and conflicts over what can—or can’t—be “reduced” to data. Contributors discuss the intellectual history of data as a concept; describe early financial modeling and some unusual sources for astronomical data; discover the prehistory of the database in newspaper clippings and index cards; and consider contemporary “dataveillance” of our online habits as well as the complexity of scientific data curation."]]></description>
<dc:subject>to:NB books:noted data_analysis data_sets history_of_science data_mining history_of_technology history_of_statistics to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:70c12e553636/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_technology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/euclid.ba/1340371071">
    <title>Fienberg : When did Bayesian inference become &quot;Bayesian&quot;?</title>
    <dc:date>2013-05-06T19:29:52+00:00</dc:date>
    <link>http://projecteuclid.org/euclid.ba/1340371071</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["While Bayes' theorem has a 250-year history, and the method of inverse probability that flowed from it dominated statistical thinking into the twentieth century, the adjective "Bayesian" was not part of the statistical lexicon until relatively recently. This paper provides an overview of key Bayesian developments, beginning with Bayes' posthumously published 1763 paper and continuing up through approximately 1970, including the period of time when "Bayesian" emerged as the label of choice for those who advocated Bayesian methods."]]></description>
<dc:subject>to:NB history_of_statistics history_of_science bayesianism fienberg.stephen_e. kith_and_kin have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a3fae5fea25d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fienberg.stephen_e."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1301.3578">
    <title>[1301.3578] Cramer-Rao Lower Bound and Information Geometry</title>
    <dc:date>2013-01-17T03:50:23+00:00</dc:date>
    <link>http://arxiv.org/abs/1301.3578</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article focuses on an important piece of work of the world renowned Indian statistician, Calyampudi Radhakrishna Rao. In 1945, C. R. Rao (25 years old then) published a pathbreaking paper, which had a profound impact on subsequent statistical research."]]></description>
<dc:subject>to:NB statistics information_geometry estimation history_of_statistics cramer-rao_inequality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a4850f7d6b6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cramer-rao_inequality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/8333.html">
    <title>Carson, J.: The Measure of Merit: Talents, Intelligence, and Inequality in the French and American Republics, 1750-1940.</title>
    <dc:date>2012-06-17T22:04:36+00:00</dc:date>
    <link>http://press.princeton.edu/titles/8333.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How have modern democracies squared their commitment to equality with their fear that disparities in talent and intelligence might be natural, persistent, and consequential? In this wide-ranging account of American and French understandings of merit, talent, and intelligence over the past two centuries, John Carson tells the fascinating story of how two nations wrestled scientifically with human inequalities and their social and political implications.
"Surveying a broad array of political tracts, philosophical treatises, scientific works, and journalistic writings, Carson chronicles the gradual embrace of the IQ version of intelligence in the United States, while in France, the birthplace of the modern intelligence test, expert judgment was consistently prized above such quantitative measures. He also reveals the crucial role that determinations of, and contests over, merit have played in both societies--they have helped to organize educational systems, justify racial hierarchies, classify army recruits, and direct individuals onto particular educational and career paths.
"A contribution to both the history of science and intellectual history, The Measure of Merit illuminates the shadow languages of inequality that have haunted the American and French republics since their inceptions."]]></description>
<dc:subject>to:NB books:noted re:g_paper history_of_science history_of_statistics mental_testing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b3d0bde96aa8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:g_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mental_testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0801.4263">
    <title>[0801.4263] A.-M. Guerry's Moral Statistics of France: Challenges for Multivariable Spatial Analysis</title>
    <dc:date>2012-06-17T21:28:19+00:00</dc:date>
    <link>http://arxiv.org/abs/0801.4263</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Andr'{e}-Michel Guerry's (1833) Essai sur la Statistique Morale de la France was one of the foundation studies of modern social science. Guerry assembled data on crimes, suicides, literacy and other ``moral statistics,'' and used tables and maps to analyze a variety of social issues in perhaps the first comprehensive study relating such variables. Indeed, the Essai may be considered the book that launched modern empirical social science, for the questions raised and the methods Guerry developed to try to answer them. Guerry's data consist of a large number of variables recorded for each of the d'{e}partments of France in the 1820--1830s and therefore involve both multivariate and geographical aspects. In addition to historical interest, these data provide the opportunity to ask how modern methods of statistics, graphics, thematic cartography and geovisualization can shed further light on the questions he raised. We present a variety of methods attempting to address Guerry's challenge for multivariate spatial statistics."]]></description>
<dc:subject>spatial_statistics statistics history_of_statistics france to_teach:undergrad-ADA in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:daf78d42797e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:france"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0804.2996">
    <title>[0804.2996] The Epic Story of Maximum Likelihood</title>
    <dc:date>2012-06-10T22:15:03+00:00</dc:date>
    <link>http://arxiv.org/abs/0804.2996</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["At a superficial level, the idea of maximum likelihood must be prehistoric: early hunters and gatherers may not have used the words ``method of maximum likelihood'' to describe their choice of where and how to hunt and gather, but it is hard to believe they would have been surprised if their method had been described in those terms. It seems a simple, even unassailable idea: Who would rise to argue in favor of a method of minimum likelihood, or even mediocre likelihood? And yet the mathematical history of the topic shows this ``simple idea'' is really anything but simple. Joseph Louis Lagrange, Daniel Bernoulli, Leonard Euler, Pierre Simon Laplace and Carl Friedrich Gauss are only some of those who explored the topic, not always in ways we would sanction today. In this article, that history is reviewed from back well before Fisher to the time of Lucien Le Cam's dissertation. In the process Fisher's unpublished 1930 characterization of conditions for the consistency and efficiency of maximum likelihood estimates is presented, and the mathematical basis of his three proofs discussed. In particular, Fisher's derivation of the information inequality is seen to be derived from his work on the analysis of variance, and his later approach via estimating functions was derived from Euler's Relation for homogeneous functions. The reaction to Fisher's work is reviewed, and some lessons drawn."

Gated version: http://projecteuclid.org/euclid.ss/1207580174]]></description>
<dc:subject>likelihood statistics estimation history_of_statistics stigler.stephen fisher.r.a. pearson.karl neyman.jerzy hotelling.harold cramer-rao_inequality information_geometry have_read wald.abraham in_NB re:HEAS</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14527fe78fd1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stigler.stephen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher.r.a."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearson.karl"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neyman.jerzy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hotelling.harold"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cramer-rao_inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wald.abraham"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:HEAS"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0808.4032">
    <title>[0808.4032] Karl Pearson's Theoretical Errors and the Advances They Inspired</title>
    <dc:date>2012-02-29T16:04:34+00:00</dc:date>
    <link>http://arxiv.org/abs/0808.4032</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Karl Pearson played an enormous role in determining the content and organization of statistical research in his day, through his research, his teaching, his establishment of laboratories, and his initiation of a vast publishing program. His technical contributions had initially and continue today to have a profound impact upon the work of both applied and theoretical statisticians, partly through their inadequately acknowledged influence upon Ronald A. Fisher. Particular attention is drawn to two of Pearson's major errors that nonetheless have left a positive and lasting impression upon the statistical world."]]></description>
<dc:subject>to:NB statistics history_of_statistics stigler.stephen pearson.karl</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b76a32be1cde/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stigler.stephen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:pearson.karl"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoms/1177731355">
    <title>Scheffe : Statistical Inference in the Non-Parametric Case (1943)</title>
    <dc:date>2012-02-08T21:30:25+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aoms/1177731355</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[We knew nothing.]]></description>
<dc:subject>have_read statistics nonparametrics history_of_statistics estimation hypothesis_testing two-sample_tests in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e1e4a2fb000b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:two-sample_tests"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pubs.amstat.org/doi/abs/10.1198/jcgs.2011.204b">
    <title>Early Computational Statistics - Journal of Computational and Graphical Statistics - 20(4):811</title>
    <dc:date>2011-12-09T22:03:29+00:00</dc:date>
    <link>http://pubs.amstat.org/doi/abs/10.1198/jcgs.2011.204b</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I consider the beginnings of computational and empirical statistics, particularly emphasizing the contributions to these by the scientists at Los Alamos National Laboratory during and after World War II. The timeline considered herein begins with preparations for the 1890 U.S. Census and concludes with Tukey’s introduction of the jackknife."]]></description>
<dc:subject>to_read statistics history_of_mathematics history_of_statistics computational_statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d7726f6fbdf5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:computational_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mpra.ub.uni-muenchen.de/34117/">
    <title>From Wald to Savage: homo economicus becomes a Bayesian statistician - Munich Personal RePEc Archive</title>
    <dc:date>2011-10-17T18:56:32+00:00</dc:date>
    <link>http://mpra.ub.uni-muenchen.de/34117/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Bayesian rationality is the paradigm of rational behavior in neoclassical economics. A rational agent in an economic model is one who maximizes her subjective expected utility and consistently revises her beliefs according to Bayes’s rule. The paper raises the question of how, when and why this characterization of rationality came to be endorsed by mainstream economists. Though no definitive answer is provided, it is argued that the question is far from trivial and of great historiographic importance. The story begins with Abraham Wald’s behaviorist approach to statistics and culminates with Leonard J. Savage’s elaboration of subjective expected utility theory in his 1954 classic The Foundations of Statistics. It is the latter’s acknowledged fiasco to achieve its planned goal, the reinterpretation of traditional inferential techniques along subjectivist and behaviorist lines, which raises the puzzle of how a failed project in statistics could turn into such a tremendous hit in economics. A couple of tentative answers are also offered, involving the role of the consistency requirement in neoclassical analysis and the impact of the postwar transformation of US business schools."  --- The guess about business schools at the end seems plausible.]]></description>
<dc:subject>re:phil-of-bayes_paper bayesianism statistics decision_theory economics history_of_statistics history_of_economics have_read wald.abraham savage.leonard_j. foundations_of_statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a7fe66fd71e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:phil-of-bayes_paper"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bayesianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:decision_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:wald.abraham"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:savage.leonard_j."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:foundations_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=272888">
    <title>SSRN-Neyman's Smooth Test and Its Applications in Econometrics by Aurobindo Ghosh, Anil Bera</title>
    <dc:date>2010-10-10T21:59:56+00:00</dc:date>
    <link>http://papers.ssrn.com/sol3/papers.cfm?abstract_id=272888</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I can rarely remember such _enthusiasm_ in a statistical paper.
]]></description>
<dc:subject>hypothesis_testing statistics neyman.jerzy econometrics history_of_statistics have_read goodness-of-fit mis-specification_testing</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ab9582d6fee/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:neyman.jerzy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goodness-of-fit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mis-specification_testing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.powells.com/biblio/9780387878560">
    <title>Powell's Books - History of the Central Limit Theorem: From Laplace to Donsker by Hans Fischer</title>
    <dc:date>2010-05-31T14:32:50+00:00</dc:date>
    <link>http://www.powells.com/biblio/9780387878560</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This study discusses the history of the central limit theorem and related probabilistic limit theorems from about 1810 through 1950. In this context the book also describes the historical development of analytical probability theory and its tools, such as characteristic functions or moments. The central limit theorem was originally deduced by Laplace as a statement about approximations for the distributions of sums of independent random variables within the framework of classical probability, which focused upon specific problems and applications."
]]></description>
<dc:subject>books:noted history_of_mathematics history_of_statistics probability central_limit_theorem coveted</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d5d05f02f8a4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coveted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.0188">
    <title>[1003.0188] History of applications of martingales in survival analysis</title>
    <dc:date>2010-04-26T19:12:44+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0188</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>martingales statistics history_of_statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:301e341cb1de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:martingales"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://mqup.mcgill.ca/book.php?bookid=2376">
    <title>A Total Science: Statistics in Liberal and Fascist Italy</title>
    <dc:date>2010-04-01T01:54:56+00:00</dc:date>
    <link>http://mqup.mcgill.ca/book.php?bookid=2376</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[http://bactra.org/weblog/algae-2010-04.html
]]></description>
<dc:subject>history_of_science history_of_statistics fascism italy sociology_of_science totalitarianism books:reviewed have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:432e89abd6e1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fascism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:italy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:totalitarianism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:reviewed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081">
    <title>Lehmann: On the history and use of some standard statistical models</title>
    <dc:date>2009-12-31T17:38:18+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.imsc/1207580081</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["his paper tries to tell the story of the general linear model, which saw the light of day 200 years ago, and the assumptions underlying it. We distinguish three principal stages (ignoring earlier more isolated instances). The model was first proposed in the context of astronomical and geodesic observations, where the main source of variation was observational error. This was the main use of the model during the 19th century.

In the 1920’s it was developed in a new direction by R.A. Fisher whose principal applications were in agriculture and biology. Finally, beginning in the 1930’s and 40’s it became an important tool for the social sciences. As new areas of applications were added, the assumptions underlying the model tended to become more questionable, and the resulting statistical techniques more prone to misuse."
]]></description>
<dc:subject>regression linear_regression history_of_statistics statistics have_read</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c474b34f5636/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>