Bootstrap and wild bootstrap for high dimensional linear models
In this paper 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→∞. The range of validit...
Enregistré dans:
| Auteur principal: | |
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| Format: | Article (Journal) |
| Langue: | anglais |
| Publié: |
1993
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| In: |
The annals of statistics
Year: 1993, Volume: 21, Numéro: 1, Pages: 255-285 |
| ISSN: | 2168-8966 |
| Accès en ligne: | Verlag, Volltext: http://dx.doi.org./10.1214/aos/1176349025 Verlag, Volltext: http://www.jstor.org/stable/3035590 Verlag, Volltext: https://projecteuclid.org/download/pdf_1/euclid.aos/1176349025 
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| Notes sur l'auteur: | Enno Mammen |
| Résumé: | In this paper 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→∞. 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. |
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| Description: | First available in Project Euclid: 12 April 2007 Gesehen am 26.02.2018 |
| Description matérielle: | Online Resource |
| ISSN: | 2168-8966 |