Adaptive sequential selection procedures for optimal quantile with control variates

Shing Chih Tsai and Guangxin Jiang

European Journal of Operational Research, 2025, vol. 326, issue 3, 515-529

Abstract: This paper introduces adaptive sequential selection procedures leveraging control variate quantile estimators for efficient quantile-based ranking and selection in simulation studies. Two variations are proposed: one simplifies estimation using binary control variates, and the other employs a discrete approximation to derive a post-stratified control variate quantile estimator. Theoretical analysis establishes the asymptotic validity and efficiency of these methods, including a novel central limit theorem for the post-stratified estimator. Numerical experiments on normal distributions and a basic queueing problem demonstrate the superior performance and adaptability of the proposed procedures. This work advances the integration of variance reduction techniques into quantile-based ranking-and-selection procedures, providing a robust framework for practical applications.

Keywords: Simulation; Ranking and selection; Quantile selection; Control variates; Post-stratified sampling; Variance reduction techniques (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:326:y:2025:i:3:p:515-529

DOI: 10.1016/j.ejor.2025.05.049

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