Optimal Stopping of a Random Sequence with Unknown Distribution

Alexander Goldenshluger () and Assaf Zeevi ()
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Alexander Goldenshluger: Department of Statistics, University of Haifa, Haifa, 3498838, Israel
Assaf Zeevi: Graduate School of Business, Columbia University, New York, New York 10027

Mathematics of Operations Research, 2022, vol. 47, issue 1, 29-49

Abstract: The subject of this paper is the problem of optimal stopping of a sequence of independent and identically distributed random variables with unknown distribution. We propose a stopping rule that is based on relative ranks and study its performance as measured by the maximal relative regret over suitable nonparametric classes of distributions. It is shown that the proposed rule is first-order asymptotically optimal and nearly rate optimal in terms of the rate at which the relative regret converges to zero. We also develop a general method for numerical solution of sequential stopping problems with no distributional information and use it in order to implement the proposed stopping rule. Some numerical experiments illustrating performance of the rule are presented as well.

Keywords: Primary: 60G40; secondary: 62L15; optimal stopping; secretary problems; extreme-value distributions; relative ranks; no information; minimax regret (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormoor:v:47:y:2022:i:1:p:29-49

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