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On the impact of model selection on predictor identification and parameter inference

Content Provider	Springer Nature Link
Author	Pfeiffer, Ruth M. Redd, Andrew Carroll, Raymond J.
Copyright Year	2016
Abstract	We assessed the ability of several penalized regression methods for linear and logistic models to identify outcome-associated predictors and the impact of predictor selection on parameter inference for practical sample sizes. We studied effect estimates obtained directly from penalized methods (Algorithm 1), or by refitting selected predictors with standard regression (Algorithm 2). For linear models, penalized linear regression, elastic net, smoothly clipped absolute deviation (SCAD), least angle regression and LASSO had a low false negative (FN) predictor selection rates but false positive (FP) rates above 20 % for all sample and effect sizes. Partial least squares regression had few FPs but many FNs. Only relaxo had low FP and FN rates. For logistic models, LASSO and penalized logistic regression had many FPs and few FNs for all sample and effect sizes. SCAD and adaptive logistic regression had low or moderate FP rates but many FNs. 95 % confidence interval coverage of predictors with null effects was approximately 100 % for Algorithm 1 for all methods, and 95 % for Algorithm 2 for large sample and effect sizes. Coverage was low only for penalized partial least squares (linear regression). For outcome-associated predictors, coverage was close to 95 % for Algorithm 2 for large sample and effect sizes for all methods except penalized partial least squares and penalized logistic regression. Coverage was sub-nominal for Algorithm 1. In conclusion, many methods performed comparably, and while Algorithm 2 is preferred to Algorithm 1 for estimation, it yields valid inference only for large effect and sample sizes.
Starting Page	667
Ending Page	690
Page Count	24
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	32
Issue Number	2
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-10-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biased estimates Post-model selection inference Finite sample inference Shrinkage Variable selection Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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