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    <title>[1604.08403] Bayesian functional linear regression with sparse step functions</title>
    <dc:date>2016-05-08T15:33:21+00:00</dc:date>
    <link>http://arxiv.org/abs/1604.08403</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[The functional linear regression model is a common tool to determine the relationship between a scalar outcome and a functional predictor. This paper focuses on the interpretability of the estimation of the coefficient function of this model. We propose a Bayesian functional Linear regression with Sparse Step functions (Bliss). The aim of the method is to provide interpretable estimates: the Bayesian model is based on an adaptive decomposition of the coefficient function into sparse and simple functions. A Bayes estimator is constructed with a specific loss function. The method is compared to its competitors on simulated datasets and is illustrated on a black P\'erigord truffle dataset.
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<dc:subject>statistics linear-regression models algorithms horse-races nudge-targets to-understand consider:looking-to-see</dc:subject>
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    <title>[0812.3141] Choosing a penalty for model selection in heteroscedastic regression</title>
    <dc:date>2010-06-19T12:44:20+00:00</dc:date>
    <link>http://arxiv.org/abs/0812.3141</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["We consider the problem of choosing between several models in least-squares regression with heteroscedastic data. We prove that any penalization procedure is suboptimal when the penalty is a function of the dimension of the model, at least for some typical heteroscedastic model selection problems. In particular, Mallows' Cp is suboptimal in this framework. On the contrary, optimal model selection is possible with data-driven penalties such as resampling or $V$-fold penalties. Therefore, it is worth estimating the shape of the penalty from data, even at the price of a higher computational cost. Simulation experiments illustrate the existence of a trade-off between statistical accuracy and computational complexity. As a conclusion, we sketch some rules for choosing a penalty in least-squares regression, depending on what is known about possible variations of the noise-level."
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<dc:subject>statistics statistical-tests linear-regression meta-optimization nudge-targets multiobjective-optimization pragmatism-it-ain't</dc:subject>
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