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Asymptotic Duration for Optimal Multiple Stopping Problems

Author

Listed:
  • Hugh N. Entwistle

    (School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia)

  • Christopher J. Lustri

    (School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia)

  • Georgy Yu. Sofronov

    (School of Mathematical and Physical Sciences, Macquarie University, Sydney, NSW 2109, Australia)

Abstract

We study the asymptotic duration of optimal stopping problems involving a sequence of independent random variables that are drawn from a known continuous distribution. These variables are observed as a sequence, where no recall of previous observations is permitted, and the objective is to form an optimal strategy to maximise the expected reward. In our previous work, we presented a methodology, borrowing techniques from applied mathematics, for obtaining asymptotic expressions for the expectation duration of the optimal stopping time where one stop is permitted. In this study, we generalise further to the case where more than one stop is permitted, with an updated objective function of maximising the expected sum of the variables chosen. We formulate a complete generalisation for an exponential family as well as the uniform distribution by utilising an inductive approach in the formulation of the stopping rule. Explicit examples are shown for common probability functions as well as simulations to verify the asymptotic calculations.

Suggested Citation

  • Hugh N. Entwistle & Christopher J. Lustri & Georgy Yu. Sofronov, 2024. "Asymptotic Duration for Optimal Multiple Stopping Problems," Mathematics, MDPI, vol. 12(5), pages 1-12, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:652-:d:1344524
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    References listed on IDEAS

    as
    1. Demers, Simon, 2021. "Expected duration of the no-information minimum rank problem," Statistics & Probability Letters, Elsevier, vol. 168(C).
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