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M-estimation in linear models under nonstandard conditions. (English) Zbl 1038.62026

Summary: The limiting distribution of M-estimators of the regression parameter in linear models is derived under nonstandard conditions, allowing, e.g., for discontinuities in density functions. Unlike usual regularity assumptions, our conditions are satisfied, for instance, in the case of regression quantiles, hence also in the context of \(L_1\) estimation; our results thus extend those of K. Knight [Ann. Stat. 26, 755–770 (1998; Zbl 0929.62021)]. The resulting asymptotic distributions, in general, are not Gaussian. Therefore, the limiting bootstrap distributions of these estimators are also investigated. It is shown that bootstrap approximations are correct to the first order only when limiting distributions are Gaussian, or along specific sequences \(m_n\) of bootstrap sample sizes. Numerical examples are given to illustrate these asymptotic results.

MSC:

62F12 Asymptotic properties of parametric estimators
62F40 Bootstrap, jackknife and other resampling methods
62E20 Asymptotic distribution theory in statistics
62J05 Linear regression; mixed models

Citations:

Zbl 0929.62021

Software:

bootstrap
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Full Text: DOI

References:

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