Title :
Outlier-resistant algorithms for detecting a change in a stochastic process
Author :
Bansal, Rakesh K. ; Papantoni-Kazakos, P.
Author_Institution :
Dept. of Electr. Eng., Virginia Univ., Charlottesville, VA, USA
fDate :
5/1/1989 12:00:00 AM
Abstract :
Outlier-resistant algorithms that detect a change from a given nominal stationary process to another such process are given. The nominal processes are assumed to be mutually independent and to satisfy some general regularity conditions. The outlier sequences are assumed to be independently and identically distributed and independent of the nominal processes. The proposed algorithms are sequential and consist of uniformly bounded steps. The asymptotic performance of the algorithms is analyzed, both in the absence and the presence of outliers. Breakdown points and influence functions are defined and analyzed. The algorithms are studied in more detail for Gaussian autoregressive nominal processes
Keywords :
information theory; stochastic processes; Gaussian autoregressive nominal processes; asymptotic performance; breakdown points; independently and identically distributed; influence functions; nominal stationary process; outlier sequences; outlier-resistant algorithms; stochastic process; uniformly bounded steps; Algorithm design and analysis; Change detection algorithms; Electric breakdown; Fault detection; Image edge detection; Performance analysis; Pollution measurement; Random variables; Stochastic processes; Testing;
Journal_Title :
Information Theory, IEEE Transactions on