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


Personal Author(s) : Papantoni-Kazakos, P ; Bansal, Rakesh K


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a198290.pdf


Report Date : Mar 1988


Pagination or Media Count : 12


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.


Descriptors :   *ALGORITHMS , *STOCHASTIC PROCESSES , ATTENTION , OPERATION , MIXING , CONTAMINATION , CONTINUITY , TIME , DETECTION , MEMORY DEVICES


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE