Accession Number:

ADA244414

Title:

Goodness of Fit Tests for Spectral Distributions

Descriptive Note:

Technical rept.,

Corporate Author:

STANFORD UNIV CA DEPT OF STATISTICS

Personal Author(s):

Report Date:

1991-10-01

Pagination or Media Count:

39.0

Abstract:

The spectral distribution function of a stationary stochastic process standardized by dividing by the variance of the process is a linear function of the autocorrelations. The integral of the sample standardized spectral density periodogram is a similar linear function of the autocorrelations. As the sample size increases, the difference of these two functions multiplied by the square root of the sample size converges is weakly to a Gaussian stochastic process with a continuous time parameter. A monotonic transformation of this parameter yields a Brownian bridge plus an independent radom term. The distributions of functionals of this process are the limiting distributions of goodness of fit criteria that are used for testing hypotheses about the process autocorrelations. An application is to tests of independence flat spectrum. The characteristic function of the Cramer-von Miese statistic is obtained inequalities for the Kolmogorow-Smirnov criterion are given. Confidence regions for unspecified process distributions are found.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE