An Investigation of Nonlinear Controls and Regression-Adjusted Estimators for Variance Reduction in Computer Simulation

Abstract

This dissertation develops new techniques for variance reduction in computer simulation. It demonstrates that applying nonlinear transformations to control variables can increase their effectiveness over linear controls. It shows how one can reduce the variance of quantile estimates, where the quantile of interest is a continuous and strictly monotone transformation of the control quantile, by transforming the control quantile with a different continuous and strictly monotone transformation. Asymptotic expansions are developed to validate the improved performance of the nonlinear control for the quantile estimate. Finally, in the realm of regenerative simulation, regression-adjusted techniques are applied to controlled regenerative estimates. The resulting estimates have a greatly reduced estimated mean square error. (Author)

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1991
Accession Number
ADA242716

Entities

People

  • Richard L. Ressler

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Simulations
  • Data Science
  • Differential Equations
  • Distribution Functions
  • Equations
  • Information Science
  • Normal Distribution
  • Order Statistics
  • Partial Differential Equations
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computational Modeling and Simulation