Dimension asymptotics for generalised bootstrap in linear regression

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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
Issue number2
StatePublished - Oct 9 2002


  • Bootstrap
  • Dimension asymptotics
  • Jackknife
  • Regression


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