BOOTSTRAP AND WILD BOOTSTRAP FOR HIGH-DIMENSIONAL LINEAR-MODELS

• 成果类型: Article
• 署名作者: MAMMEN, E
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176349025
• 发表日期: 1993
• 页码: 255-285
• 关键词: p-regression parameters; asymptotic-behavior; Nonparametric Regression; resampling methods; robust regression; M-ESTIMATORS; jackknife; error; p2/n

摘要

In this paper two bootstrap procedures are considered for the estimation of the distribution of linear contrasts and of F-test statistics in high dimensional linear models. An asymptotic approach will be chosen where the dimension p of the model may increase for sample size n --> infinity. The range of validity will be compared for the normal approximation and for the bootstrap procedures. Furthermore, it will be argued that the rates of convergence are different for the bootstrap procedures in this asymptotic framework. This is in contrast to the usual asymptotic approach where p is fixed.