Prediction and estimation consistency of sparse multi-class penalized optimal scoring

被引：6

作者：

Gaynanova, Irina ^{[1
]}

机构：

[1] Texas A&M Univ, Dept Stat, MS 3143, College Stn, TX 77843 USA

来源：

BERNOULLI | 2020年 / 26卷 / 01期

基金：

美国国家科学基金会;

关键词：

classification; high-dimensional regression; lasso; linear discriminant analysis; ORACLE INEQUALITIES; DISCRIMINANT-ANALYSIS; VARIABLE SELECTION; MODEL SELECTION; REGRESSION; RECOVERY;

D O I：

10.3150/19-BEJ1126

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：

Sparse linear discriminant analysis via penalized optimal scoring is a successful tool for classification in high-dimensional settings. While the variable selection consistency of sparse optimal scoring has been established, the corresponding prediction and estimation consistency results have been lacking. We bridge this gap by providing probabilistic bounds on out-of-sample prediction error and estimation error of multi-class penalized optimal scoring allowing for diverging number of classes.

引用

页码：286 / 322

页数：37

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