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2015 Inference from small and big data sets with error rates
Miklós Csörgő, Masoud M. Nasari
Electron. J. Statist. 9(1): 535-566 (2015). DOI: 10.1214/15-EJS1011


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.


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Miklós Csörgő. Masoud M. Nasari. "Inference from small and big data sets with error rates." Electron. J. Statist. 9 (1) 535 - 566, 2015.


Published: 2015
First available in Project Euclid: 24 March 2015

zbMATH: 1309.62036
MathSciNet: MR3326134
Digital Object Identifier: 10.1214/15-EJS1011

Primary: 62E20
Secondary: 62G09

Rights: Copyright © 2015 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.9 • No. 1 • 2015
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