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用 EM 算法拟合双重抽样分类数据的对数线性模型AI 解读

Double-sampling experiments can be expressed as incomplete multiway contingency tables and analyzed by using techniques appropriate for fitting log-linear models. The framework described can be applied to experiments with any number of factors and measurement devices. Maximum likelihood estimation via the EM algorithm leads to straightforward expressions for covariances of estimates of the parameters and functions of the parameters.

用 EM 算法拟合双重抽样分类数据的对数线性模型AI 解读

Double-sampling experiments can be expressed as incomplete multiway contingency tables and analyzed by using techniques appropriate for fitting log-linear models. The framework described can be applied to experiments with any number of factors and measurement devices. Maximum likelihood estimation via the EM algorithm leads to straightforward expressions for covariances of estimates of the parameters and functions of the parameters.

列联表交互作用检验：最小 Logit 卡方估计与最大似然估计的比较AI 解读

This article compares small sample properties of 12 goodness of fit tests for interaction in 2×2×2 and 3×2×2 contingency tables. The 12 tests are constructed by-combining three tests (the minimum logit chi-square test, the Pearson chi-square test, and the likelihood ratio test) and four methods of estimation (the iterative maximum likelihood estimator and three variants of the non-iterative minimum logit chi-square estimator). An evaluation is made of the adequacy of the…

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