对数线性模型下基于Φ-散度测度的均值滑动检验  

Mean-shift Test Based on Φ-divergence Measure for Log-linear Model

作　　者：金应华[1] 向思源 Jin Ying-hua;Xiang Si-yuan(School of Applied Mathematics,Guangdong University of Technology,Guangzhou 510520,China)

机构地区：[1]广东工业大学应用数学学院,广东广州510520

出　　处：《广东工业大学学报》2018年第4期32-36,44,共6页Journal of Guangdong University of Technology

基　　金：国家自然科学基金资助项目(11401114);广东省自然科学基金资助项目(S2012040007622)

摘　　要：研究了对数线性模型的均值滑动检验.基于Φ-散度和最小Φ-散度估计提出了3类检验统计量,它们是似然比检验统计量和Pearson检验统计量的推广.研究了这3类统计量的渐近分布,并用此理论结果分析了一组实际数据.最后通过模拟研究表明,在小样本量下,这3类统计量中有比似然比检验统计量和Pearson检验统计量表现更好的统计量.The mean-shift test under the log-linear mode is studied. Based on Φ-divergence and the minimum Φ-divergence estimator, three families of test statistic, which are a generalization of log-likelihood ratio statistic and the Pearson statistic, are proposed. Their asymptotic distribution is presented while they are used to analyze some empirical data. A simulation study is also conducted. And the outcome shows that there are alternatives among these three families of test statistic as good as（or even better than） the log-likelihood ratio statistic and the Pearson statistic under finite sample size.

关 键 词：对数线性模型 Φ-散度 最小Φ-散度估计 均值滑动检验 

分 类 号：O175[理学—数学]