Biometrical Letters Vol. 54(1), 2017, pp. 1-24
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AN ALGORITHM FOR A NEW METHOD OF CHANGE-POINT ANALYSIS IN THE INDEPENDENT POISSON SEQUENCE Chihiro Hirotsu1, Harukazu Tsuruta2 1Collaborative Research Center, Meisei University, 2-1-1, Hodokubo, Hino-city, Tokyo 191-8506, Japan, e-mail: hirotsu@ge.meisei-u.ac.jp 2Tokyo Metropolitan Institute of Gerontology, 35-2 Sakae-cho, Itabashi-ku, Tokyo 173-0015, Japan |
Step change-point and slope change-point models in the independent Poisson sequence are developed based on accumulated and doubly-accumulated statistics. The method for the step change-point model developed in Section 2 is an alternative to the likelihood ratio test of Worsley (1986) and the algorithm for p-value calculation based on the first-order Markov property is the same as that given there. Different algorithms for the non-null distribution and inference on the change-point itself are, however, newly developed and a Pascal program is given in the Appendix. These methods are extended to the slope change-point model in Section 3. The approach is essentially the same as that of Section 2 but the algorithm is now based on the second-order Markov property and becomes a little more complicated. The Pascal program related to the slope change-point model is supported on the website, URL: https://corec.meisei-u.ac.jp/labs/hirotsu/.
Convexity hypothesis; Markov property; Monotone hypothesis; Slope change-point model; Step change-point model
