On Edgeworth expansion and moving block bootstrap for studentized M-estimators in multiple linear regression models

被引:34
|
作者
Lahiri, SN
机构
基金
美国国家科学基金会;
关键词
edgeworth expansion; moving block bootstrap; M-estimators; multiple linear regression; stationarity; strong mixing; studentization;
D O I
10.1006/jmva.1996.0003
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
This paper considers the multiple linear regression model Y-i = x'(i) beta + epsilon(i), i = i,...,n, where x(i)'s are known p x 1 vectors, beta is a p x 1 vector of parameters, and epsilon(1), epsilon(2),... are stationary, strongly mixing random variables. Let <(beta)over bar (n)> denote an M-estimator of p corresponding to some score function psi. Under some conditions on psi, xi's and Ei's, a two-term Edgeworth expansion for Studentized multivariate M-estimator is proved. Furthermore, it is shown that the moving block bootstrap is second-order correct for some suitable bootstrap analog of Studentized <(beta)over bar (n)>. (C) 1996 Academic Press, Inc.
引用
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页码:42 / 59
页数:18
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