THE MULTINOMIAL-POISSON TRANSFORMATION
THE MULTINOMIAL-POISSON TRANSFORMATION
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DOI：
10.2307/2348134
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发表时间：
1994-01-01
期刊：
影响因子：
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通讯作者：
BAKER, SG
中科院分区：
文献类型：
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作者：
BAKER, SG
多项Poisson（MP）变换简化了多项数据模型中的极大似然估计，研究者们在专门推导的基础上，将MP变换应用于各种模型。在这里，我们提出了一个一般的推导，这是简单的比专门的推导，并允许调查人员使用MP变换容易在新的模型。我们还展示了MP变换如何适应不完整的多项式数据，以及它如何帮助找到剂量形式的最大似然估计和方差。以前的应用包括对数线性模型，捕获-再捕获模型，比例风险模型与分类协变量和Rasch模型的推广。新的应用包括计算方差的比值比的对数，一个模型的选民多元化，条件logistic回归匹配集和两阶段的病例对照研究。
The multinomial-Poisson (MP) transformation simplifies maximum likelihood estimation in a wide variety of models for multinomial data, On the basis of specialized derivations, investigators have applied the MP transformation to various models. Here we present a general derivation, which is simpler than the specialized derivations and allows investigators to use the MP transformation readily in new models. We also show how the MP transformation can accommodate incomplete multinomial data and how it can assist in finding dosed form maximum likelihood estimates and variances. Previous applications include log-linear models, capture-recapture models, proportional hazards models with categorical covariates and generalizations of the Rasch model. New applications include computing the variance of the logarithm of the odds ratio, a model for voter plurality, conditional logistic regression for matched sets and two-stage case-control studies.