Monte Carlo Simulation with Censored Sampling

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2020
Accession Number
AD1126331

Entities

People

  • Ezra W. Akin

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Data Mining
  • Data Science
  • Estimators
  • Information Science
  • Knowledge Management
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Numerical Analysis
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Surveys
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design