Asymptotics of Selective Inference. (7th March 2017)

• Record Type: Journal Article
• Authors: Tian, Xiaoying; Taylor, Jonathan
• Is Part Of: Scandinavian journal of statistics. Volume 44:Number 2(2017:Jun.)
• Page Start: 480
• Page End: 499
• Publication Date: 2017-03-07
• Subjects: high‐dimensional inference, LASSO, non‐Gaussian error, selective inference; Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12261
• Languages: English
• ISSNs: 0303-6898

Abstract

Abstract: In this paper, we seek to establish asymptotic results for selective inference procedures removing the assumption of Gaussianity. The class of selection procedures we consider are determined by affine inequalities, which we refer to as affine selection procedures. Examples of affine selection procedures include selective inference along the solution path of the least absolute shrinkage and selection operator (LASSO), as well as selective inference after fitting the least absolute shrinkage and selection operator at a fixed value of the regularization parameter. We also consider some tests in penalized generalized linear models. Our result proves asymptotic convergence in the high‐dimensional setting where n < p, and n can be of a logarithmic factor of the dimension p for some procedures.