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Adaptive sequential selection procedures for optimal quantile with control variates

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  • Tsai, Shing Chih
  • Jiang, Guangxin

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.

Suggested Citation

  • Tsai, Shing Chih & Jiang, Guangxin, 2025. "Adaptive sequential selection procedures for optimal quantile with control variates," European Journal of Operational Research, Elsevier, vol. 326(3), pages 515-529.
    • Handle: RePEc:eee:ejores:v:326:y:2025:i:3:p:515-529
    DOI: 10.1016/j.ejor.2025.05.049
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