On general resampling algorithms and their performance in distribution estimation
Recent work of several authors has focussed on first-order properties (e.g., consistency) of general bootstrap algorithms, where the numbers of times that data values are resampled form an exchangeable sequence. In the present paper we develop second-order properties of such algorithms, in a very ge...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
1994
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| In: |
The annals of statistics
Year: 1994, Volume: 22, Issue: 4, Pages: 2011-2030 |
| ISSN: | 2168-8966 |
| DOI: | 10.1214/aos/1176325769 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1214/aos/1176325769 Verlag, Volltext: https://projecteuclid.org/euclid.aos/1176325769 Verlag, Volltext: https://projecteuclid.org/download/pdf_1/euclid.aos/1176325769 
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| Author Notes: | Peter Hall, Enno Mammen |
MARC
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| 520 | |a Recent work of several authors has focussed on first-order properties (e.g., consistency) of general bootstrap algorithms, where the numbers of times that data values are resampled form an exchangeable sequence. In the present paper we develop second-order properties of such algorithms, in a very general setting. Performance is discussed in the context of distribution estimation, and formulae for higher-order moments and cumulants are developed. Arguing thus, necessary and sufficient conditions are given for general resampling algorithms to correctly capture second-order properties. | ||
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