文章基本信息

标题：Inference from small and big data sets with error rates
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作者：Miklós Csörgő ; Masoud M. Nasari
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2015
卷号：9
期号：1
页码：535-566
DOI：10.1214/15-EJS1011
语种：English
出版社：Institute of Mathematical Statistics
摘要：In this paper we introduce randomized $t$-type statistics that will be referred to as randomized pivots. We show that these randomized pivots yield central limit theorems with a significantly smaller error as compared to that of their classical counterparts under the same conditions. This constitutes a desirable result when a relatively small number of data is available. When a data set is too big to be processed, or when it constitutes a random sample from a super-population, we use our randomized pivots to infer about the mean based on significantly smaller sub-samples. The approach taken is shown to relate naturally to estimating distributions of both small and big data sets.
关键词：Randomized t-pivots;Berry-Ess´een bounds;im proved CLT’s;small and moderate samples.