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...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
1993
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| In: |
The annals of statistics
Year: 1993, Volume: 21, Issue: 1, Pages: 255-285 |
| ISSN: | 2168-8966 |
| Online Access: | 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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| Author Notes: | Enno Mammen |
| Summary: | 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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| Item Description: | First available in Project Euclid: 12 April 2007 Gesehen am 26.02.2018 |
| Physical Description: | Online Resource |
| ISSN: | 2168-8966 |