正态分布方差变点的检验

Three Tests for a Change-point in Variance of Normal Distribution

  • 摘要: 本文讨论了正态分布方差只有一个变点的检验问题,我们构造了三个检验统计量,其中L检验基于非参数U统计量,B检验基于Bayes方法,R检验由极大似然比方法导出。本文给出了LBR检验的渐近临界值,并用MonteCarlo模拟方法研究了这三个检验与平方的CUSUM检验以及LM检验的势,并进行了比较。当变点在序列的前一半位置时,LR检验较好,当变点在序列的后一半位置时,平方的CUSUMB检验较好。

     

    Abstract: This paper considers the problem of testing for a change-point in variance of the sequence of normal random variables with unknown mean. We construct three tests, namely, L-test based on Lehmann’s (1951) non-parametric U-statistic, B-test based on Bayesian method and R-test derived from the maximum likelihood ratio method. The approximate critical values of L, B, R tests are calculated. Monte Carlo simulation studies were carried out to calculate power of these tests, the CUSUM of squares test (Brown et al, 1975) and the LM-test (Nyblom, 1989). An empirical power comparison of the above tests suggests that when change-point is between the beginning and the mid portion, L and R tests are better, when changes-point is between the mid.portion and the end, CUSUM of squares and B tests are better.

     

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