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A self-normalization test for a change-point in the shape parameter of a gamma distributed sequence
Changchun Tan,Yuehua Wu,Cuiling Dong,Baiqi Miao 한국통계학회 2013 Journal of the Korean Statistical Society Vol.42 No.3
In this paper, a parametric change-point problem encountered in analyzing a gamma distributed sequence is considered. We propose a self-normalization based CUSUM type test statistic to detect the presence of a change-point, and obtain its limiting null distribution. The CUSUM-based procedure for detecting the location of this change-point is also given. At the same time, simulation results are provided, which show that our procedures are effective.
Tan Changchun,Hu Junying,Wu Yuehua 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.1
In this paper, the multiple change-point problem in the scale parameter of a sequence of independent gamma distributed observations is discussed. A reversible jump Markov chain Monte Carlo (RJMCMC) algorithm is developed to compute the posterior probabilities of the number and positions of the multiple change-points. Four types of jumps are designed, and the acceptance probability of each type is given. The simulation studies show that the RJMCMC-based method is efcient in the detection of multiple change-points in the scale parameter in gamma distributed sequence, and performs better than a self-normalization based method. In addition, a real data example about successive rises and falls of Shanghai stock exchange composite index yield is used to illustrate the proposed methodology