MONTE CARLO SIMULATION WITH CENSORED SAMPLING
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Authors
Akin, Ezra W.
Subjects
Monte Carlo
random censoring
control variates
maximum likelihood
simulation
stratification
random censoring
control variates
maximum likelihood
simulation
stratification
Advisors
Kress, Moshe
Szechtman, Roberto
Atkinson, Michael P.
Wilcox, Lucas C.
Glazebrook, Kevin, Lancaster University
Date of Issue
2020-09
Date
Sep-20
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
We consider Monte Carlo simulation in a setting where the samples are subject to random censoring. Such censoring occurs in settings as varied and diverse as perimeter protection, survival analysis, and electro-magnetic spectrum monitoring. We introduce and analyze two estimators: one based on empirical likelihood methods and another rooted in control variates ideas. We show that the proposed estimators can dramatically reduce the estimator variance in relation to the crude Monte Carlo estimator while not sacrificing computational speed.
Type
Thesis
Description
Series/Report No
Department
Operations Research (OR)
Organization
Identifiers
NPS Report Number
Sponsors
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Format
Citation
Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States