对数线性模型下基于φ-散度测度的均值滑动检验

doi:10.12052/gdutxb.170179

广东工业大学学报 ›› 2018, Vol. 35 ›› Issue (04): 32-36.doi: 10.12052/gdutxb.170179

对数线性模型下基于φ-散度测度的均值滑动检验

金应华, 向思源

广东工业大学应用数学学院, 广东广州 510520

收稿日期:2018-01-02 出版日期:2018-07-09 发布日期:2018-05-24
作者简介:金应华(1982-),男,讲师,博士,主要研究方向为对数线性模型和分位数回归模型.
基金资助:
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）

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

Jin Ying-hua, Xiang Si-yuan

School of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China

Received:2018-01-02 Online:2018-07-09 Published:2018-05-24

摘要/Abstract

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.

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

Abstract: 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.

Key words: log-linear model, φ-divergence, minimum φ-divergence estimator, mean-shift test

中图分类号:

O175

金应华, 向思源. 对数线性模型下基于φ-散度测度的均值滑动检验[J]. 广东工业大学学报, 2018, 35(04): 32-36.

Jin Ying-hua, Xiang Si-yuan. Mean-shift Test Based on φ-divergence Measure for Log-linear Model[J]. Journal of Guangdong University of Technology, 2018, 35(04): 32-36.

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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

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