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列联表的多项分布—泊松齐次模型AI 解读

A unified approach to maximum likelihood inference for a broad, new class of contingency table models is presented. The model class comprises multinomial-Poisson homogeneous (MPH) models, which can be characterized by an independent sampling plan and a system of homogeneous constraints, h(m) = 0, where m is the vector of expected table counts. Maximum likelihood (ML) fitting and large-sample inference for MPH models are described. The MPH models are partitioned into…

摘要暂缺

多元分类响应的关联—边际建模：极大似然方法AI 解读

Generalized log-linear models can be used to describe the association structure and/or the marginal distributions of multivariate categorical responses. We simultaneously model the association structure and marginal distributions using association-marginal (AM) models, which are specially formulated generalized log-linear models that combine two models: an association (A) model, which describes the association among all the responses; and a marginal (M) model, which…

多元分类响应模型拟合优度统计量的分解AI 解读

Numerical and asymptotic stochastic partitioning of goodness-of-fit statistics are considered for a broad class of simultaneous multivariate categorical response models. These simultaneous models impose constraints on the joint and marginal distributions of categorical response variables. Under certain conditions, the tenability of the corresponding simultaneous hypothesis can be assessed by separately testing the two subhypotheses: one regarding the joint distributions and…

一类广义对数线性模型的极大似然方法AI 解读

We discuss maximum likelihood methods for fitting a broad class of multivariate categorical response data models. In particular, we derive the large-sample distributions for maximum likelihood estimators of parameters of product-multinomial generalized log-linear models. The large-sample behavior of other relevant likelihood-based statistics such as goodness-of-fit statistics and adjusted residuals is also described. The asymptotic results are derived within the framework of…

多项与泊松对数线性模型的比较AI 解读

SUMMARY We introduce a method for comparing multinomial and Poisson log-linear models which affords an explicit description of their equivalences and differences. The method involves specifying the model in terms of constraint equations, rather than the more common freedom equations. The Poisson and multinomial large sample distributions of log-linear model parameter estimators are derived and compared within this constraint equation context; reparameterizations are thereby…

多元分类响应的联合分布与边际分布同步建模AI 解读

We discuss model-fitting methods for analyzing simultaneously the joint and marginal distributions of multivariate categorical responses. The models are members of a broad class of generalized logit and loglinear models. We fit them by improving a maximum likelihood algorithm that uses Lagrange's method of undetermined multipliers and a Newton-Raphson iterative scheme. We also discuss goodness-of-fit tests and adjusted residuals, and give asymptotic distributions of model…

含个体特定效应的重复有序类别响应比例优势模型AI 解读

Suppose subjects make repeated responses on the same ordered categorical scale. We propose a generalization of the Rasch model that expresses the cumulative logit of the response distribution using subject parameters and a proportional odds structure for item effects. Parameters in the model describe subject-specific, rather than population averaged, effects. Consistent estimation of the effects requires eliminating the subject parameters. We accomplish this by simultaneous…

摘要暂缺

不完整数据下泊松对数线性模型的观测信息矩阵AI 解读

We consider inferences about Poisson log linear models for which only certain disjoint sums of the data are observable. We derive an explicit formula for the observed information matrix associated with the log linear parameters that is intuitively appealing and simple to evaluate.