Dimension asymptotics for generalised bootstrap in linear regression

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The consistency of a class of generalized bootstrap techniques for the distribution of the least squares parameter estimator in linear regression was examined. The data size and the regressors were assumed to be random in the analysis. The results of the analysis showed that best results were obtained with resampling techniques assuming that the number of parameters tend to infinity.

Original languageEnglish (US)
Pages (from-to)367-381
Number of pages15
JournalAnnals of the Institute of Statistical Mathematics
Volume54
Issue number2
DOIs
StatePublished - Oct 9 2002

Fingerprint

Asymptotic Dimension
Linear regression
Bootstrap
Resampling
Least Squares
Infinity
Tend
Estimator
Class

Keywords

  • Bootstrap
  • Dimension asymptotics
  • Jackknife
  • Regression

Cite this

Dimension asymptotics for generalised bootstrap in linear regression. / Chatterjee, Snigdhansu; Bose, Arup.

In: Annals of the Institute of Statistical Mathematics, Vol. 54, No. 2, 09.10.2002, p. 367-381.

Research output: Contribution to journalArticle

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