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Bootstrap and wild bootstrap for high dimensional linear models. (English) Zbl 0771.62032
Summary: Two bootstrap procedures are considered for the estimation of the distribution of linear contrasts and of \(F\)-test statistics in high dimensional linear models. An asymptotic approach will be chosen where the dimension \(p\) of the model may increase for sample size \(n\to \infty\). The range of validity will be compared for the normal approximation and for the bootstrap procedures. Furthermore, it will be argued that the rates of convergence are different for the bootstrap procedures in this asymptotic framework. This is in contrast to the usual asymptotic approach where \(p\) is fixed.

MSC:
62G09 Nonparametric statistical resampling methods
62F12 Asymptotic properties of parametric estimators
62J05 Linear regression; mixed models
62E20 Asymptotic distribution theory in statistics
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