Pinboard (cshalizi)
https://pinboard.in/u:cshalizi/public/
recent bookmarks from cshalizi
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[2309.10140] A Geometric Framework for Neural Feature Learning
2023-12-08T14:18:57+00:00
https://arxiv.org/abs/2309.10140
cshalizi
to:NB information_geometry variable_selection neural_networks statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:47874c9b2e08/
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LASSO-driven inference in time and space
2021-08-10T14:07:16+00:00
https://projecteuclid.org/journals/annals-of-statistics/volume-49/issue-3/LASSO-driven-inference-in-time-and-space/10.1214/20-AOS2019.short
cshalizi
to:NB lasso sparsity regression time_series spatial_statistics variable_selection statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:86f9e897007b/
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[1911.01850] Stabilizing Variable Selection and Regression
2021-05-24T22:54:41+00:00
https://arxiv.org/abs/1911.01850
cshalizi
to:NB variable_selection regression prediction statistics generalizability buhlmann.peter
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:92e2cf839bf8/
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[2101.01603] Comparing methods addressing multi-collinearity when developing prediction models
2021-01-06T16:51:35+00:00
https://arxiv.org/abs/2101.01603
cshalizi
to:NB linear_regression variable_selection re:TALR
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:91c2d6d885ca/
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[2012.04171] Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
2021-01-03T19:48:45+00:00
https://arxiv.org/abs/2012.04171
cshalizi
to:NB variable_selection sparsity factor_analysis additive_models statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c88603e68cdb/
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Baker , Tang , Allen : Feature selection for data integration with mixed multiview data
2020-12-19T16:42:14+00:00
https://projecteuclid.org/euclid.aoas/1608346892
cshalizi
to:NB variable_selection lasso sparsity statistics allen.genevera_i.
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c18ec82da65c/
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[2011.12154] Identifying important predictors in large data bases -- multiple testing and model selection
2020-11-25T14:26:20+00:00
https://arxiv.org/abs/2011.12154
cshalizi n are introduced and compared with a variety of penalized likelihood methods, in particular SLOPE and SLOBE. The focus is on methods which control the FDR in terms of model identification. Theoretical results are provided both with respect to model identification and prediction and various simulation results are presented which illustrate the performance of the different methods in different situations."]]>
to:NB multiple_testing variable_selection high-dimensional_statistics statistics regression
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:04b56518e578/
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[2002.06060] Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
2020-08-20T15:50:02+00:00
https://arxiv.org/abs/2002.06060
cshalizi
to:NB to_read causal_inference statistics variable_selection neuroscience peters.jonas methodological_advice
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:4b63620b847f/
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High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking | SpringerLink
2020-02-23T15:45:43+00:00
https://link.springer.com/article/10.1007/s11222-019-09914-9
cshalizi
to:NB regression prediction statistics high-dimensional_statistics lasso to_teach:linear_models re:TALR variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:0959893a9ed3/
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[1910.14212] Sobolev Independence Criterion
2019-11-11T20:03:54+00:00
https://arxiv.org/abs/1910.14212
cshalizi
to:NB dependence_measures statistics variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:62d8f23fed2e/
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[1910.12327] A simple measure of conditional dependence
2019-10-29T02:24:47+00:00
https://arxiv.org/abs/1910.12327
cshalizi
to:NB dependence_measures statistics variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:aa7ecdf18065/
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[1910.00174] Randomized Ablation Feature Importance
2019-10-02T15:21:03+00:00
https://arxiv.org/abs/1910.00174
cshalizi
to:NB prediction variable_selection statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:2e63850756a8/
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IPAD: Stable Interpretable Forecasting with Knockoffs Inference: Journal of the American Statistical Association: Vol 0, No 0
2019-09-18T12:36:47+00:00
https://www.tandfonline.com/doi/full/10.1080/01621459.2019.1654878
cshalizi
to:NB statistics regression high-dimensional_statistics factor_analysis variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c602c00b2afc/
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[1612.08468] Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
2019-08-21T13:12:57+00:00
https://arxiv.org/abs/1612.08468
cshalizi
to:NB variable_selection visual_display_of_quantitative_information statistics regression
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:9eb5cd7d3c48/
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High-Dimensional Adaptive Minimax Sparse Estimation With Interactions - IEEE Journals & Magazine
2019-08-20T15:51:54+00:00
https://ieeexplore.ieee.org/document/8700269
cshalizi
to:NB statistics high-dimensional_statistics regression sparsity variable_selection linear_regression
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:e3bf750aafd7/
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[1507.03133] Best Subset Selection via a Modern Optimization Lens
2019-08-20T14:48:29+00:00
https://arxiv.org/abs/1507.03133
cshalizi
to:NB optimization variable_selection statistics via:tslumley
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:cd5299ec8dca/
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[1907.07384] Feature Selection via Mutual Information: New Theoretical Insights
2019-07-18T23:27:29+00:00
https://arxiv.org/abs/1907.07384
cshalizi
variable_selection information_theory statistics to_teach:data-mining in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:1e47f8cec091/
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[1906.01990] A Model-free Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection
2019-06-06T13:45:37+00:00
https://arxiv.org/abs/1906.01990
cshalizi
linear_regression regression statistics variable_selection in_NB color_me_skeptical
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:bb93af1c2633/
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[1811.00645] The Holdout Randomization Test: Principled and Easy Black Box Feature Selection
2019-05-30T16:04:56+00:00
https://arxiv.org/abs/1811.00645
cshalizi
cross-validation variable_selection statistics blei.david have_read in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:e4ed13c6dd3c/
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[1905.10573] Selective inference after variable selection via multiscale bootstrap
2019-05-28T16:46:26+00:00
https://arxiv.org/abs/1905.10573
cshalizi
variable_selection post-selection_inference statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:801500782dac/
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[1801.03896] Robust inference with knockoffs
2018-09-13T16:38:52+00:00
https://arxiv.org/abs/1801.03896
cshalizi
regression variable_selection statistics samworth.richard_j. knockoffs to_teach:linear_models in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:888d5db086e8/
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Oracle M-Estimation for Time Series Models - Giurcanu - 2016 - Journal of Time Series Analysis - Wiley Online Library
2017-04-04T13:16:54+00:00
http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12221/abstract
cshalizi
bootstrap time_series statistics estimation in_NB sparsity variable_selection high-dimensional_statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:7728d02c1d9a/
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[0906.4391] KNIFE: Kernel Iterative Feature Extraction
2016-11-30T02:04:49+00:00
https://arxiv.org/abs/0906.4391
cshalizi
statistics regression variable_selection data_mining to_teach:data-mining kernel_methods in_NB heard_the_talk
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:061ce2697602/
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[1507.05315] Confidence Sets Based on the Lasso Estimator
2015-08-05T15:13:50+00:00
http://arxiv.org/abs/1507.05315
cshalizi
lasso regression confidence_sets model_selection variable_selection statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:800bb38e54a8/
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[1406.0052] Variable selection in high-dimensional additive models based on norms of projections
2014-07-12T00:22:46+00:00
http://arxiv.org/abs/1406.0052
cshalizi
additive_models variable_selection hilbert_space statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:5e6c4de3a03e/
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[1404.2007] A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection
2014-04-20T18:12:43+00:00
http://arxiv.org/abs/1404.2007
cshalizi
variable_selection model_selection lasso high-dimensional_statistics nobel.andrew statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:f3a8dfd23a3c/
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[1403.7063] A Significance Test for Covariates in Nonparametric Regression
2014-04-03T18:29:39+00:00
http://arxiv.org/abs/1403.7063
cshalizi
variable_selection hypothesis_testing statistics nonparametrics regression to_teach:undergrad-ADA in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:d2e07646c1a5/
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[1403.7023] Worst possible sub-directions in high-dimensional models
2014-04-01T21:19:01+00:00
http://arxiv.org/abs/1403.7023
cshalizi
to:NB high-dimensional_statistics lasso sparsity variable_selection statistics van_de_geer.sara
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:d88ae59edb90/
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[1403.4296] Inference for feature selection using the Lasso with high-dimensional data
2014-03-22T19:27:24+00:00
http://arxiv.org/abs/1403.4296
cshalizi
lasso regression variable_selection re:what_is_the_right_null_model_for_linear_regression high-dimensional_statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:5559a8fb3f09/
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[1403.4544] On the Sensitivity of the Lasso to the Number of Predictor Variables
2014-03-21T15:52:20+00:00
http://arxiv.org/abs/1403.4544
cshalizi
lasso regression variable_selection high-dimensional_statistics cross-validation statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c0b013c52c01/
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[1401.8097] An Algorithm for Nonlinear, Nonparametric Model Choice and Prediction
2014-02-03T20:29:28+00:00
http://arxiv.org/abs/1401.8097
cshalizi
model_selection regression nonparametrics variable_selection statistics hall.peter in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:cd1695d5b681/
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[1312.1706] Swapping Variables for High-Dimensional Sparse Regression from Correlated Measurements
2014-01-02T18:27:15+00:00
http://arxiv.org/abs/1312.1706
cshalizi
to:NB high-dimensional_statistics lasso sparsity variable_selection statistics vats.divyanshu
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c1912ea2ab6c/
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[1312.1473] Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models
2013-12-26T00:33:14+00:00
http://arxiv.org/abs/1312.1473
cshalizi
lasso time_series variable_selection statistics re:your_favorite_dsge_sucks in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:7fcc3eb7d15b/
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[1312.5556] Hierarchical Testing in the High-Dimensional Setting with Correlated Variables
2013-12-23T16:31:50+00:00
http://arxiv.org/abs/1312.5556
cshalizi
to:NB to_read high-dimensional_statistics variable_selection buhlmann.peter hierarchical_statistical_models hierarchical_structure
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:332c500f5bc7/
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[1310.4887] Variable Selection Inference for Bayesian Additive Regression Trees
2013-10-23T14:26:06+00:00
http://arxiv.org/abs/1310.4887
cshalizi
statistics high-dimensional_statistics variable_selection regression kith_and_kin jensen.shane george.ed in_NB gene_expression_data_analysis
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:cb14a0bcfae3/
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[1309.2068] Modified Cross-Validation for Penalized High-Dimensional Linear Regression Models
2013-09-10T18:24:51+00:00
http://arxiv.org/abs/1309.2068
cshalizi
cross-validation lasso regression statistics high-dimensional_statistics variable_selection in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:809b6dd19675/
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The Steep Price of Sparsity « Normal Deviate
2013-07-29T19:52:06+00:00
http://normaldeviate.wordpress.com/2013/07/27/the-steep-price-of-sparsity/
cshalizimodel_selection track_down_references statistics sparsity variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:1185172426ca/
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[1306.6557] Optimal Feature Selection in High-Dimensional Discriminant Analysis
2013-06-30T03:40:07+00:00
http://arxiv.org/abs/1306.6557
cshalizi
classifiers high-dimensional_statistics sparsity variable_selection statistics liu.han kolar.mladen in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:b9f4ee25efd5/
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[1306.5505] Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression
2013-06-27T15:19:31+00:00
http://arxiv.org/abs/1306.5505
cshalizi
to:NB lasso regression variable_selection high-dimensional_statistics statistics estimation yu.bin
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c95bb73aab6b/
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[1304.5678] Analytic Feature Selection for Support Vector Machines
2013-04-23T22:31:26+00:00
http://arxiv.org/abs/1304.5678
cshalizi
to:NB variable_selection data_mining to_teach:data-mining text_mining classifiers
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:c6d333cf0207/
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[1304.5245] Feature Elimination in empirical risk minimization and support vector machines
2013-04-22T17:21:47+00:00
http://arxiv.org/abs/1304.5245
cshalizi
to:NB variable_selection classifiers learning_theory
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:8abc27f2cd48/
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[0906.4391] KNIFE: Kernel Iterative Feature Extraction
2012-11-03T15:56:41+00:00
http://arxiv.org/abs/0906.4391
cshalizi
to:NB statistics machine_learning allen.genevera_i. regression classifiers kernel_methods to_teach:data-mining to_teach:undergrad-ADA have_read variable_selection
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:fce2678790d0/
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Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR
2012-09-30T13:44:42+00:00
http://people.ee.duke.edu/~lcarin/OSCAR.pdf
cshalizi
to:NB regression variable_selection statistics via:ryantibs
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:3d45c29a3a2b/
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[1208.2572] Nonparametric sparsity and regularization
2012-09-04T02:09:59+00:00
http://arxiv.org/abs/1208.2572
cshalizi
to:NB to_read nonparametrics regression variable_selection sparsity statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:5404e1979240/
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[1205.6843] Significance Testing and Group Variable Selection
2012-06-23T15:17:19+00:00
http://arxiv.org/abs/1205.6843
cshalizi
variable_selection model_selection regression nonparametrics to_teach:undergrad-ADA in_NB kernel_smoothing
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:1e1c49d015a0/
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[1206.2696] Flexible Variable Selection for Recovering Sparsity in Nonadditive Nonparametric Models
2012-06-23T15:09:41+00:00
http://arxiv.org/abs/1206.2696
cshalizi
to:NB regression nonparametrics additive_models variable_selection sparsity statistics to_read to_teach:undergrad-ADA
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:7b43e31d14f3/
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A Confidence Region Approach to Tuning for Variable Selection - Journal of Computational and Graphical Statistics - Volume 21, Issue 2
2012-06-23T14:56:55+00:00
http://www.tandfonline.com/doi/abs/10.1080/10618600.2012.679890
cshalizi
to:NB variable_selection regression statistics confidence_sets lasso
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:69f5fef13f87/
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Variable selection with error control: another look at stability selection - Shah - 2012 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library
2012-06-23T14:30:40+00:00
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.01034.x/abstract
cshalizi
to:NB variable_selection statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:6b054752ae8b/
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[1206.4682] Copula-based Kernel Dependency Measures
2012-06-23T14:19:27+00:00
http://arxiv.org/abs/1206.4682
cshalizi
information_theory entropy_estimation poczos.barnabas variable_selection machine_learning copulas kernel_methods in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:617bbe4e33dd/
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[1206.4680] Fast Prediction of New Feature Utility
2012-06-23T14:18:26+00:00
http://arxiv.org/abs/1206.4680
cshalizi
machine_learning prediction regression classifiers variable_selection have_read in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:3461e24caec3/
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[0801.1158] Hierarchical selection of variables in sparse high-dimensional regression
2012-06-17T21:26:12+00:00
http://arxiv.org/abs/0801.1158
cshalizi
to:NB variable_selection high-dimensional_statistics regression statistics re:what_is_the_right_null_model_for_linear_regression bickel.peter_j.
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:045bc9afe3b3/
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[1205.6761] Nonparametric Model Checking and Variable Selection
2012-06-07T16:02:38+00:00
http://arxiv.org/abs/1205.6761
cshalizi
variable_selection regression nonparametrics statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:5b928ac92a1f/
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Feature Selection via Dependence Maximization
2012-06-07T15:59:35+00:00
http://jmlr.csail.mit.edu/papers/v13/song12a.html
cshalizi
to:NB variable_selection hilbert_space machine_learning information_theory
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:93feda6cd8e0/
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[1206.1024] Conditional Sure Independence Screening
2012-06-07T15:41:24+00:00
http://arxiv.org/abs/1206.1024
cshalizi
to:NB variable_selection high-dimensional_statistics statistics
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:4b61d8167ea5/
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[0805.1179] Autoregressive Process Modeling via the Lasso Procedure
2012-03-04T17:12:03+00:00
http://arxiv.org/abs/0805.1179
cshalizi
time_series statistics lasso sparsity variable_selection kith_and_kin heard_the_talk rinaldo.alessandro nardi.yuval in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:519c50513386/
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[1102.3616] Tight conditions for consistent variable selection in high dimensional nonparametric regression
2012-02-27T00:02:40+00:00
http://arxiv.org/abs/1102.3616
cshalizi
regression variable_selection nonparametrics statistics in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:8b693691cb40/
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Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
2012-02-10T18:10:39+00:00
http://jmlr.csail.mit.edu/papers/v13/brown12a.html
cshalizi
information_theory statistics variable_selection model_selection to_teach:data-mining to:blog machine_learning classifiers have_read in_NB graphical_models
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:3584eb0c3974/
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Nonparametric estimation of the link function including variable selection - Gerhard Tutz and Sebastian Petry - Statistics and Computing, Volume 22, Number 2
2011-12-01T12:54:28+00:00
http://www.springerlink.com/content/582v131176130h06/
cshalizi
regression variable_selection statistics nonparametrics to_read to_teach:undergrad-ADA in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:6413527c3ed3/
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Variance estimation using refitted cross-validation in ultrahigh dimensional regression - Fan - 2011 - Journal of the Royal Statistical Society: Series B (Statistical Methodology) - Wiley Online Library
2011-10-10T12:16:53+00:00
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9868.2011.01005.x/abstract
cshalizi
statistics regression variable_selection cross-validation estimation fan.jianqing variance_estimation in_NB
https://pinboard.in/
https://pinboard.in/u:cshalizi/b:f86a2929f2a4/
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A Framework for Unbiased Model Selection Based on Boosting
2011-07-12T17:40:02+00:00
http://pubs.amstat.org/doi/abs/10.1198/jcgs.2011.09220
cshalizimodel_selection variable_selection boosting ensemble_methods statistics
https://pinboard.in/u:cshalizi/b:6492001fa93c/
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Statistics for High-Dimensional Data
2011-07-05T13:18:31+00:00
http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-642-20191-2?cm_mmc=NBA-_-Jul-11_WEST_8259992-_-product-_-978-3-642-20191-2
cshalizistatistics machine_learning variable_selection lasso sparsity buhlmann.peter van_de_geer.sara books:recommended books:owned
https://pinboard.in/u:cshalizi/b:71f041b9a3fb/
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[1106.5242] High Dimensional Sparse Econometric Models: An Introduction
2011-06-28T14:57:54+00:00
http://arxiv.org/abs/1106.5242
cshalizi
regression lasso variable_selection econometrics
https://pinboard.in/u:cshalizi/b:a5ec335e85a0/
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Radchenko , James : Improved variable selection with Forward-Lasso adaptive shrinkage
2011-03-23T13:40:05+00:00
http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aoas/1300715197
cshalizilasso sparsity regression variable_selection model_selection to:NB
https://pinboard.in/u:cshalizi/b:7aef4dc9dfd2/
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[1009.2302] The Predictive Lasso
2010-09-16T01:54:03+00:00
http://arxiv.org/abs/1009.2302
cshalizi
regression lasso variable_selection sparsity information_theory statistics
https://pinboard.in/u:cshalizi/b:9bcc63589a6c/
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"Partial Generalized Additive Models: An Information-Theoretic Approach for Dealing With Concurvity and Selecting Variables" (Gu, Kenny, Zhu, 2010)
2010-09-16T01:52:49+00:00
http://pubs.amstat.org/doi/abs/10.1198/jcgs.2010.07139
cshalizi
regression additive_models information_theory variable_selection statistics
https://pinboard.in/u:cshalizi/b:92d9d94d50d9/
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions
2010-02-05T05:28:31+00:00
http://jmlr.csail.mit.edu/papers/v11/aliferis10b.html
cshalizigraphical_models causal_inference variable_selection classifiers machine_learning statistics
https://pinboard.in/u:cshalizi/b:9ad623c602b8/
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Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
2010-02-05T05:28:13+00:00
http://jmlr.csail.mit.edu/papers/v11/aliferis10a.html
cshalizigraphical_models causal_inference variable_selection classifiers machine_learning statistics
https://pinboard.in/u:cshalizi/b:6bef18f55d93/
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[0909.0844] High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning
2009-09-15T17:59:15+00:00
http://arxiv.org/abs/0909.0844
cshalizivariable_selection regression kernel_methods statistics machine_learning in_NB
https://pinboard.in/u:cshalizi/b:59c474a0ed79/
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[0909.1308] Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
2009-09-08T11:22:28+00:00
http://arxiv.org/abs/0909.1308
cshalizimachine_learning sparsity random_fields conditional_random_fields to:NB variable_selection graphical_models
https://pinboard.in/u:cshalizi/b:7c173069e09d/
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[0909.0635] Advances in Feature Selection with Mutual Information
2009-09-06T02:26:01+00:00
http://arxiv.org/abs/0909.0635
cshaliziinformation_theory statistics machine_learning variable_selection to:NB to_teach:data-mining have_read
https://pinboard.in/u:cshalizi/b:09ede2ad5c9c/