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

摘要

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.