GOODNESS OF FIT TESTS FOR THE MULTIPLE LOGISTIC REGRESSION-MODEL
GOODNESS OF FIT TESTS FOR THE MULTIPLE LOGISTIC REGRESSION-MODEL
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DOI：
10.1080/03610928008827941
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发表时间：
1980-01-01
期刊：
影响因子：
--
通讯作者：
LEMESHOW, S
中科院分区：
文献类型：
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作者：
HOSMER, DW;LEMESHOW, S
本文提出了几种检验统计量来评估多元Logistic回归模型的拟合优度。通过对列联表应用卡方检验获得检验统计量，其中使用两种不同的分组策略和两组不同的分布假设确定预期频率。应用摩尔斯普鲁伊尔（1975）的卡方检验理论和计算机模拟检验了这些统计量的零分布。所有的统计数据显示有卡方分布或分布，可以很好地近似卡方。的自由度取决于特定的统计和分布的假设。使用计算机模拟的正态，线性和指数替代模型的每个建议的统计检查的权力。
Several test statistics are proposed for the purpose of assessing the goodness of fit of the multiple logistic regression model. The test statistics are obtained by applying a chi-square test for a contingency table in which the expected frequencies are determined using two different grouping strategies and two different sets of distributional assumptions. The null distributions of these statistics are examined by applying the theory for chi-square tests of Moore Spruill (1975) and through computer simulations. All statistics are shown to have a chi-square distribution or a distribution which can be well approximated by a chi-square. The degrees of freedom are shown to depend on the particular statistic and the distributional assumptions.The power of each of the proposed statistics is examined for the normal, linear, and exponential alternative models using computer simulations.