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Extremal large deviations in controlled i.i.d. processes with applications to hypothesis testing

Part of: Limit theorems

Published online by Cambridge University Press:  01 July 2016

Nahum Shimkin
Nahum Shimkin*
Affiliation:
University of Minnesota
*
* Postal address: Institute for Mathematics and its Applications, University of Minnesota, Minneapolis, MN 55455, USA.
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Abstract

We consider a controlled i.i.d. process, where several i.i.d. sources are sampled sequentially. At each time instant, a controller determines from which source to obtain the next sample. Any causal sampling policy, possibly history-dependent, may be employed. The purpose is to characterize the extremal large deviations of the sample mean, namely to obtain asymptotic rate bounds (similar to and extending Cramér's theorem) which hold uniformly over all sampling policies. Lower and upper bounds are obtained, and it is shown that in many (but not all) cases stationary sampling policies are sufficient to obtain the extremal large deviations rates. These results are applied to a hypothesis testing problem, where data samples may be sequentially chosen from several i.i.d. sources (representing different types of experiments). The analysis provides asymptotic estimates for the error probabilities, corresponding both to optimal and to worst-case sampling policies.

Keywords

LARGE DEVIATIONS OF THE SAMPLE MEANEXTREMAL RATESSTATIONARY POLICIESSEQUENTIAL DESIGN

MSC classification

Primary: 60F10: Large deviations
Secondary: 90C40: Markov and semi-Markov decision processes

Information

Type
Research Article
Information
Advances in Applied Probability , Volume 25 , Issue 4 , December 1993 , pp. 875 - 894
Copyright
Copyright © Applied Probability Trust 1993 

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