Sample-Path Large Deviations for Unbounded Additive Functionals of the Reflected Random Walk

Author:

Bazhba Mihail1,Blanchet Jose2,Rhee Chang-Han3ORCID,Zwart Bert45ORCID

Affiliation:

1. Quantitative Economics, University of Amsterdam, 1012 WP Amsterdam, Netherlands;

2. Management Science and Engineering, Stanford University, Stanford, California 94305;

3. Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208;

4. Stochastics Group, Centrum Wiskunde & Informatica, 1098 XG Amsterdam, Netherlands;

5. Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands

Abstract

We prove a sample-path large deviation principle (LDP) with sublinear speed for unbounded functionals of certain Markov chains induced by the Lindley recursion. The LDP holds in the Skorokhod space [Formula: see text] equipped with the [Formula: see text] topology. Our technique hinges on a suitable decomposition of the Markov chain in terms of regeneration cycles. Each regeneration cycle denotes the area accumulated during the busy period of the reflected random walk. We prove a large deviation principle for the area under the busy period of the Markov random walk, and we show that it exhibits a heavy-tailed behavior. Funding: The research of B. Zwart and M. Bazhba is supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 639.033.413]. The research of J. Blanchet is supported by the National Science Foundation (NSF) [Grants 1915967, 1820942, and 1838576] as well as the Defense Advanced Research Projects Agency [Grant N660011824028]. The research of C.-H. Rhee is supported by the NSF [Grant CMMI-2146530].

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

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