AISM 54, 367-381
© 2002 ISM
(Received May 17, 1999; revised May 29, 2000)
Abstract. We prove consistency of a class of generalised bootstrap techniques for the distribution of the least squares parameter estimator in linear regression, when the number of parameters tend to infinity with data size and the regressors are random. We show that best results are obtainable with resampling techniques that have not been considered earlier in the literature.
Key words and phrases: Bootstrap, jackknife, regression, dimension asymptotics.
Source (TeX , DVI )