Accession Number:

ADA278799

Title:

Cramer-Von Mises Variance Estimators for Simulations

Descriptive Note:

Technical rept.

Corporate Author:

NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF ADMINISTRATIVE SCIENCES

Personal Author(s):

• Goldsman, David
• Kang, Keebom
• Seila, Andrew F.

Report Date:

1993-09-01

Pagination or Media Count:

27.0

Abstract:

We study estimators for the variance parameter sigma2 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.

Descriptors:

• *ANALYSIS OF VARIANCE
• *ESTIMATES
• *ORDER STATISTICS
• *WEIGHTING FUNCTIONS
• SIMULATION
• STOCHASTIC PROCESSES
• STATIONARY
• ASYMPTOTIC NORMALITY
• YIELD
• POLYNOMIALS
• TIME
• PARAMETERS
• THEORY
• TIME SERIES ANALYSIS

Subject Categories:

• Numerical Mathematics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE