Efficient Sampling Allocation Procedures for Optimal Quantile Selection

Yijie Peng (), Chun-Hung Chen (), Michael C. Fu (), Jian-Qiang Hu () and Ilya O. Ryzhov ()
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Yijie Peng: Department of Management Science and Information Systems, Guanghua School of Management, Peking University, Beijing 100871, China
Chun-Hung Chen: Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia 22030
Michael C. Fu: Institute for Systems Research, Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Jian-Qiang Hu: Department of Management Science, Fudan University, Shanghai 200433, China
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742

INFORMS Journal on Computing, 2021, vol. 33, issue 1, 230-245

Abstract: We propose a dynamic sampling allocation and selection paradigm for finding the alternative with the optimal quantile in a Bayesian framework. Myopic allocation policies (MAPs), analogous to existing methods in classic ranking and selection for selecting the alternative with the optimal mean, and computationally efficient selection policies are derived for selecting the alternative with the optimal quantile. Under certain conditions, we prove that the proposed MAPs and selection procedures are consistent, which means that the best quantile would be eventually correctly selected as the sample size goes to infinity. Numerical experiments demonstrate that the proposed schemes can significantly improve the performance.

Keywords: ranking and selection; quantile optimization; Bayesian framework; dynamic sampling allocation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:1:p:230-245

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