On Edgeworth Expansion and Moving Block Bootstrap for StudentizedM-Estimators in Multiple Linear Regression Models

Abstract

This paper considers the multiple linear regression modelYi=xi'[beta]+[var epsilon]i,i=i, ..., n, wherexi's are knownp-1 vectors,[beta]is ap-1 vector of parameters, and[var epsilon]1,[var epsilon]2, ... are stationary, strongly mixing random variables. Let[beta]ndenote anM-estimator of[beta]corresponding to some score function[psi]. Under some conditions on[psi],xi's and[var epsilon]i's, a two-term Edgeworth expansion for Studentized multivariateM-estimator is proved. Furthermore, it is shown that the moving block bootstrap is second-order correct for some suitable bootstrap analog of Studentized[beta]n.Edgeworth expansion moving block bootstrap M-estimators multiple linear regression stationarity strong mixing Studentization (null)