Variance Reduction Using Nonlinear Control and Transformations

Abstract

Nonlinear regression-adjusted control variables are investigated for improving variance reduction in statistical and system simulations. To this end, simple control variables are piecewise sectioned and then transformed using linear and nonlinear transformations. Optimal parameters of these transformations are selected using linear or nonlinear leastsquares regression algorithms. As an example, piecewise power-transformed variables are used in the estimation of the mean for the two-variable Anderson-Darling goodness-of-fit statistic. Substantial variance reduction over straightforward controls is obtained. These parametric transformations are compared against optimal, additive nonparametric transformations obtained by using the ACE algorithm and are shown, in comparison to the results from ACE, to be nearly optimal.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1988
Accession Number
ADA200471

Entities

People

  • Peter A. Lewis
  • R. Kevin Wood
  • Richard L. Ressler

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Computational Complexity
  • Computational Science
  • Data Science
  • Information Science
  • Military Research
  • Monte Carlo Method
  • Operations Research
  • Order Statistics
  • Probability
  • Random Variables
  • Schools
  • Security
  • Standards
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Approximation Theory.
  • Statistical inference.