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.
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Chatterjee, S., Bose, A. Dimension Asymptotics for Generalised Bootstrap in Linear Regression. Annals of the Institute of Statistical Mathematics 54, 367–381 (2002). https://doi.org/10.1023/A:1022430203955
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DOI: https://doi.org/10.1023/A:1022430203955