Cramer-Von Mises Variance Estimators for Simulations

Abstract

We study estimators for the variance parameter sigma(2) of a stationary process. The estimators are based on weightings yield estimators that are 'first-order unbiased' for sigma (2) We derive an expression for the asymptotic variance of the new estimators; this expression is then used to obtain the first-order unbiased estimator having the smallest variance among fixed-degree polynomial weighting functions. Although our work is based on asymptotic theory, we present exact and empirical examples to demonstrate the new estimators' small-sample robustness. Simulation, Stationary process, Variance estimation, Standardized time series, Cramer-von mises estimator.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA278799

Entities

People

  • Andrew F. Seila
  • David Goldsman
  • Keebom Kang

Organizations

  • Naval Postgraduate School

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Convergence
  • Covariance
  • Data Science
  • Estimators
  • Industrial Engineering
  • Information Science
  • Operations Research
  • Polynomials
  • Probability
  • Random Variables
  • Stationary Processes
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes
  • Systems Engineering
  • Weighting Functions

Fields of Study

  • Mathematics

Readers

  • Fluid Dynamics.
  • Regression Analysis.