Technical Note—On Estimating Quantile Sensitivities via Infinitesimal Perturbation Analysis

Guangxin Jiang () and Michael C. Fu ()
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Guangxin Jiang: Department of Mathematics, Tongji University, Shanghai, 200092, China
Michael C. Fu: Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742

Operations Research, 2015, vol. 63, issue 2, 435-441

Abstract: Hong (2009) [Hong LJ (2009) Estimating quantile sensitivities. Oper. Res. 57(1):118-130.] introduced a general framework based on probability sensitivities and a conditional expectation relationship for estimating quantile sensitivities by infinitesimal perturbation analysis (IPA). We present an alternative more direct derivation of the IPA estimators that leads to simplified proofs for strong consistency and convergence rate of the unbatched estimator, and strong consistency and a central limit theorem for the batched estimator.

Keywords: quantile; gradient estimation; Monte Carlo simulation; sensitivity analysis (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (5)

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