SEMI-PARAMETRIC POLYNOMIAL MODIFICATION OF CUSUM ALGORITHMS FOR CHANGE-POINT DETECTION OF NON-GAUSSIAN SEQUENCES

Serhii W. Zabolotnii, Zygmunt Lech Warsza

Abstract

Expansion of logarithm likelihood ratio in the stochastic series is used to find the sequential change-point detection of non-Gaussian sequences. The moment criteria of the minimum of upper limit error probabilities sum is used to find the expansion coefficients. The proposed method is a semi-parametric type of CUSUM (cumulative sum) algorithm which needs of higher-order statistics. The experimental results show that polynomial algorithms are more effective in comparison with similar non-parametric procedures.

Keywords

change point, CUSUM alghoritm, Non-Gaussian sequence, stochastic polynomial, high order statistics

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IMEKO-WC-2015-TC21-422.pdf

DOI

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IMEKO TC

TC21 - Mathematical Tools for Measurements

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