Technical Note—On Estimating Quantile Sensitivities via Infinitesimal Perturbation Analysis

DOI10.1287/opre.2015.1356zbMath1329.65008OpenAlexW2067774568MaRDI QIDQ3453343

Michael C. Fu, Guang-Xin Jiang

Publication date: 20 November 2015

Published in: Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/opre.2015.1356

zbMATH Keywords

sensitivity analysisMonte Carlo simulationquantilegradient estimation

Mathematics Subject Classification ID

Related Items (9)

A Stochastic Approximation Method for Simulation-Based Quantile Optimization ⋮ Nonparametric inference for sensitivity of Haezendonck–Goovaerts risk measure ⋮ Computing Sensitivities for Distortion Risk Measures ⋮ Insurance Portfolio Risk Retention ⋮ Maximum Likelihood Estimation by Monte Carlo Simulation: Toward Data-Driven Stochastic Modeling ⋮ Applications of generalized likelihood ratio method to distribution sensitivities and steady-state simulation ⋮ A New Unbiased Stochastic Derivative Estimator for Discontinuous Sample Performances with Structural Parameters ⋮ Sensitivity estimation of conditional value at risk using randomized quasi-Monte Carlo ⋮ Efficient Sampling Allocation Procedures for Optimal Quantile Selection

Cites Work

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• Conditional Monte Carlo Estimation of Quantile Sensitivities
• Perturbation Analysis Gives Strongly Consistent Sensitivity Estimates for the M/G/1 Queue
• Kernel estimation of quantile sensitivities
• Estimating Quantile Sensitivities

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