A central limit theorem for bootstrap sample sums from non-i.i.d. models
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- 作者：Andrew Rosalskya ; rosalsky@stat.ufl.edu" class="auth_mail" title="E-mail the corresponding author ; Deli Lib ; dli@lakeheadu.ca" class="auth_mail" title="E-mail the corresponding author
- 关键词：60F05 ; 62G09 ; 62G20
- 刊名：Journal of Statistical Planning and Inference
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：180
- 期：Complete
- 页码：69-80
- 全文大小：519 K
文摘
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For bootstrap sample sums a very general central limit theorem is established.
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The parent sequence does not need to be independent or identically distributed.
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The main result extends a result of Liu (1988).
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A version of the main result is also obtained with random bootstrap sample size.