EXACT POST-SELECTION INFERENCE, WITH APPLICATION TO THE LASSO

被引：446

作者：

Lee, Jason D. ^{[1
]}

Sun, Dennis L. ^{[2
]}

Sun, Yuekai ^{[3
]}

Taylor, Jonathan E. ^{[4
]}

机构：

[1] Univ Calif Berkeley, 465 Soda Hall, Berkeley, CA 94720 USA

[2] Calif Polytech State Univ San Luis Obispo, Bldg 25,Room 107D, San Luis Obispo, CA USA

[3] Univ Calif Berkeley, 367 Evans Hall, Berkeley, CA 94720 USA

[4] Stanford Univ, 390 Serra Mall, Stanford, CA USA

来源：

ANNALS OF STATISTICS | 2016年 / 44卷 / 03期

基金：

美国国家科学基金会;

关键词：

Lasso; confidence interval; hypothesis test; model selection; FALSE DISCOVERY RATE; THE-LOSERS DESIGN; CONFIDENCE-INTERVALS; MODEL-SELECTION; PARAMETERS; ESTIMATORS;

D O I：

10.1214/15-AOS1371

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes. the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the selected coefficients and test whether all relevant variables have been included in the model.

引用

页码：907 / 927

页数：21

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