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重复流感暴发中两种依赖来源的分离AI 解读

Two or more responses measured on a randomly selected individual can be dependent for two quite different reasons. In the first place they can be dependent within, that is conditional upon, each individual. Secondly, dependence is created simply from the heterogeneity between individuals. This paper addresses a rare instance of being able to measure separately the dependence from the two sources. The data comprise frequencies of the infection profiles for a sample of…

分离重复流感暴发中的两种依赖来源AI 解读

Two or more responses measured on a randomly selected individual can be dependent for two quite different reasons. In the first place they can be dependent within, that is conditional upon, each individual. Secondly, dependence is created simply from the heterogeneity between individuals. This paper addresses a rare instance of being able to measure separately the dependence from the two sources. The data comprise frequencies of the infection profiles for a sample of…

列联表的马尔可夫场与对数线性交互模型AI 解读

We use a close connection between the theory of Markov fields and that of log-linear interaction models for contingency tables to define and investigate a new class of models for such tables, graphical models. These models are hierarchical models that can be represented by a simple, undirected graph on as many vertices as the dimension of the corresponding table. Further all these models can be given an interpretation in terms of conditional independence and the…

多因素列联表中的交互作用AI 解读

SUMMARY Interactions in contingency tables are compared with interactions in the analysis of variance. The comparison leads to a new formulation of the hypothesis of no second-order interaction in three-way contingency tables, to an explanation of the paradoxes associated with this hypothesis and to a comparatively simple method of constructing its likelihood-ratio test statistic. The extensions to higher-order interactions are merely outlined as they are not likely to be of…

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