An efficient estimation of nested expectations without conditional sampling

DOI10.1016/j.cam.2022.114811OpenAlexW3214921901MaRDI QIDQ2095139

Takashi Goda, Tomohiko Hironaka

Publication date: 9 November 2022

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.12278

zbMATH Keywords

Monte Carlo methodnested expectationexpected value of sample information

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Numerical methods (including Monte Carlo methods) (91G60) Statistical methods; risk measures (91G70) Monte Carlo methods (65C05) Portfolio theory (91G10)

Cites Work

• Bayesian experimental design: A review
• Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain
• Risk Estimation via Regression
• Nested Simulation in Portfolio Risk Measurement
• On a Measure of the Information Provided by an Experiment
• Multilevel Monte Carlo estimation of expected information gains
• Kernel Smoothing for Nested Estimation with Application to Portfolio Risk Measurement
• Multilevel Monte Carlo Estimation of the Expected Value of Sample Information
• Multilevel Nested Simulation for Efficient Risk Estimation

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