Accession Number:

ADA198290

Title:

Robust Algorithms for Detecting a Change in a Stochastic Process with Infinite Memory

Descriptive Note:

Technical rept. 1 Jul 1987-30 Jun 1988

Corporate Author:

VIRGINIA UNIV CHARLOTTESVILLE DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Report Date:

1988-03-01

Pagination or Media Count:

12.0

Abstract:

The authors present and discuss a class of continuous operations on the family of discrete time stochastic processes, which serves as a guide to construct qualitatively robust operations for a given class of processes, namely the one induced by a nominal process and a substitutive contaminating process. The results are general enough to help develop any robust statistical procedure, but the authors have concentrated their attention on detection of a change from one class of processes to another disjoint class of processes, while both classes consist of not necessarily Markov processes and satisfy certain mixing conditions in addition to stationarity and ergodicity. Two quantitative measures of robustness, breakdown point and influence functions are also developed for few examples.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE