Bootstrap, wild bootstrap, and asymptotic normality
Summary We show for an i.i.d. sample that bootstrap estimates consistently the distribution of a linear statistic if and only if the normal approximation with estimated variance works. An asymptotic approach is used where everything may depend onn. The result is extended to the case of independent,...
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| Main Author: | |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
1992
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| In: |
Probability theory and related fields
Year: 1992, Volume: 93, Issue: 4, Pages: 439-455$t |
| ISSN: | 1432-2064 |
| DOI: | 10.1007/BF01192716 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1007/BF01192716 Verlag, Volltext: https://link.springer.com/article/10.1007/BF01192716 Verlag, Volltext: https://link.springer.com/content/pdf/10.1007%2FBF01192716.pdf 
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| Author Notes: | Enno Mammen |
MARC
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| 520 | |a Summary We show for an i.i.d. sample that bootstrap estimates consistently the distribution of a linear statistic if and only if the normal approximation with estimated variance works. An asymptotic approach is used where everything may depend onn. The result is extended to the case of independent, but not necessarily identically distributed random variables. Furthermore it is shown that wild bootstrap works under the same conditions as bootstrap. | ||
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