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EXACT ASYMPTOTICS OF SAMPLE-MEAN-RELATED RARE-EVENT PROBABILITIES

Published online by Cambridge University Press:  16 January 2017

Julia Kuhn Open the ORCID record for Julia Kuhn [Opens in a new window] ,
Michel Mandjes  and
Thomas Taimre
Julia Kuhn
Affiliation:
Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Science Park 105, 1098 XH Amsterdam, The Netherlands School of Mathematics and Physics, The University of Queensland, St. Lucia, QLD 4072, Australia E-mail: j.kuhn@uva.nl
Michel Mandjes
Affiliation:
Korteweg-de Vries Institute for Mathematics, University of Amsterdam, Science Park 105, 1098 XH Amsterdam, The Netherlands E-mail: m.r.h.mandjes@uva.nl
Thomas Taimre
Affiliation:
School of Mathematics and Physics, The University of Queensland, St. Lucia, QLD 4072, Australia E-mail: t.taimre@uq.edu.au
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Abstract

Relying only on the classical Bahadur–Rao approximation for large deviations (LDs) of univariate sample means, we derive strong LD approximations for probabilities involving two sets of sample means. The main result concerns the exact asymptotics (as n→∞) of

$$ {\open P}\left({\max_{i\in\{1,\ldots,d_x\}}\bar X_{i,n} \les \min_{i\in\{1,\ldots,d_y\}}\bar Y_{i,n}}\right),$$
with the ${\bar X}_{i,n}{\rm s}$ (${\bar Y}_{i,n}{\rm s}$, respectively) denoting dx (dy) independent copies of sample means associated with the random variable X (Y). Assuming ${\open E}X \gt {\open E}Y$ , this is a rare event probability that vanishes essentially exponentially, but with an additional polynomial term. We also point out how the probability of interest can be estimated using importance sampling in a logarithmically efficient way. To demonstrate the usefulness of the result, we show how it can be applied to compare the order statistics of the sample means of the two populations. This has various applications, for instance in queuing or packing problems.

Keywords

applied probabilityoperations researchstochastic dynamic programmingstochastic modeling
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 2 , April 2018 , pp. 207 - 228
Copyright
Copyright © Cambridge University Press 2017 

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