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Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs

题目Generalized likelihood ratio method for stochastic models with uniform random numbers as inputs
作者彭毅杰
作者单位Peking Univ, PKU Wuhan Inst Artificial Intelligence, Guanghua Sch Management, Beijing, Peoples R China Xijiang Lab, Changsha, Peoples R China Univ Maryland, Robert H Smith Sch Business, College Pk, MD USA Univ Maryland, Inst Syst Res, College Pk, MD USA SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ, Canada Univ Rennes, INRIA, IRISA, CNRS, Campus Beaulieu, Rennes, France
关键词：GRADIENT ESTIMATION PERTURBATION ANALYSIS DERIVATIVE ESTIMATOR SENSITIVITY-ANALYSIS OPTIMIZATION SYSTEMS DESIGN
时间：2025年3月1日
出版者：EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
摘要We propose a new unbiased stochastic gradient estimator for a family of stochastic models driven by uniform random numbers as inputs. Dropping the requirement that the tails of the density of the input random variables decay smoothly, the estimator extends the applicability of the generalized likelihood ratio (GLR) method. We demonstrate the new estimator for several general classes of input random variates, including independent inverse transform random variates and dependent input random variables governed by an Archimedean copula. We show how the new estimator works in settings such as density estimation, and we illustrate applications to credit risk derivatives. Numerical experiments substantiate broad applicability and flexibility in dealing with discontinuities in the
URLhttp://hdl.handle.net/20.500.11897/727579
ISSN0377-2217
DOI10.1016/j.ejor.2024.10.001
收录情况SCI（E）
作者单位 Peking Univ, PKU Wuhan Inst Artificial Intelligence, Guanghua Sch Management, Beijing, Peoples R China Xijiang Lab, Changsha, Peoples R China Univ Maryland, Robert H Smith Sch Business, College Pk, MD USA Univ Maryland, Inst Syst Res, College Pk, MD USA SUNY Stony Brook, Dept Appl Math & Stat, Stony Brook, NY 11794 USA Univ Montreal, Dept Comp Sci & Operat Res, Montreal, PQ, Canada Univ Rennes, INRIA, IRISA, CNRS, Campus Beaulieu, Rennes, France 时间 2025年3月1日
出版者 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH URL http://hdl.handle.net/20.500.11897/727579
ISSN 0377-2217 DOI 10.1016/j.ejor.2024.10.001
收录情况 SCI（E） 分类
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