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arXiv · 1404.5671

Inference from Small and Big Data Sets with Error Rates

Miklos Csorgo·Masoud M Nasari
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Abstract

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 magnitude of 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, we use our randomized pivots to make inference 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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Miklos Csorgo, Masoud M Nasari. 2014-04-22. Inference from Small and Big Data Sets with Error Rates. https://arxiv.org/abs/1404.5671

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