Technical Note—A New Rate-Optimal Sampling Allocation for Linear Belief Models

Jiaqi Zhou () and Ilya O. Ryzhov ()
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Jiaqi Zhou: Applied Mathematics & Statistics, and Scientific Computation, University of Maryland, College Park, Maryland 20742
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742

Operations Research, 2025, vol. 73, issue 1, 239-250

Abstract: We derive a new optimal sampling budget allocation for belief models based on linear regression with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an “error” occurs if a lower-valued vector is falsely identified as being better than a higher-valued one. Our allocation optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based optimal allocation for continuous decision spaces, and it turns out to be considerably simpler and easier to implement than allocations that use discretization. We give a practicable sequential implementation and illustrate its empirical potential.

Keywords: Simulation; optimal learning; simulation optimization; linear regression; large deviations (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:73:y:2025:i:1:p:239-250

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