The variable selection problem

• 成果类型: Article
• 署名作者: George, EI
• 署名单位: University of Texas System; University of Texas Austin
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2669776
• 发表日期: 2000
• 页码: 1304-1308
• 关键词: linear-model selection; Cross-validation; INFLATION CRITERION; MULTIPLE-REGRESSION; wavelet shrinkage; schwarz criterion; Bayes Factors; uncertainty; prediction; CHOICE

摘要

The problem of variable selection is one of the most pervasive model selection problems in statistical applications. Often referred to as the problem of subset selection, it arises when one wants to model the relationship between a variable of interest and a subset of potential explanatory variables or predictors, but there is uncertainty about which subset to use. This vignette reviews some of the key developments that have led to the wide variety of approaches for this problem.