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聚类二元与连续响应联合建模的相关Probit模型AI 解读

A difficulty in joint modeling of continuous and discrete response variables is the lack of a natural multivariate distribution. For joint modeling of clustered observations on binary and continuous responses, we study a correlated probit model that has an underlying normal latent variable for the binary responses. Catalano and Ryan have factored the model into a marginal and a conditional component and used generalized estimating equations methodology to estimate the…

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多元二元响应重复测量的模型AI 解读

This article presents a logit model for a vector of binary variables observed for each subject under multiple conditions. The model contains a vector of random effects to account for correlations among the repeated measurements. A nonparametric treatment of the random effects implies a multivariate log-linear model having quasi-symmetric structure for the cross-classification of responses at the various conditions. The fit yields estimates of within-subject effects comparing…

三维列联表条件关联的改进精确推断AI 解读

We propose modified exact inferential methods for contingency tables. Ordinary “exact” inference is conservative, because of the discreteness. For estimating a common odds ratio in several 2 × 2 tables, two modifications of the ordinary “exact” confidence interval maintain at least a fixed confidence level but tend to be much narrower. One approach inverts results of a test with a modified P value utilizing the test statistic and table probabilities. The second approach…

三维列联表条件关联的改进精确推断AI 解读

We propose modified exact inferential methods for contingency tables. Ordinary “exact” inference is conservative, because of the discreteness. For estimating a common odds ratio in several 2 × 2 tables, two modifications of the ordinary “exact” confidence interval maintain at least a fixed confidence level but tend to be much narrower. One approach inverts results of a test with a modified P value utilizing the test statistic and table probabilities. The second approach…

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…

有序类别列联表的精确推断AI 解读

This article proposes an efficient numerical algorithm for small-sample exact inferences in contingency tables having ordinal classifications. The inferences, which apply conditional on the observed marginal totals, also provide an exact analysis for the log-linear model of linear-by-linear association for cell probabilities. An exact test of independence has a one-sided P value equal to the null probability that model-based maximum likelihood estimates of odds ratios are at…

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列联表关联模型中的顺序约束评分参数AI 解读

The row effects and column effects models for two-way contingency tables have parameters for the row and column categories pertaining to the association between the variables. For classifications having ordered categories, it is often reasonable to assume that the association parameters have a corresponding ordering. This article proposes order-restricted estimates of the association parameters in these models. The maximum likelihood solution can be determined by the…

有序分类数据偏关联测量的若干考量AI 解读

Some of the measures commonly used to describe association between two ordinal categorical variables, controlling for the effects of other ordinal categorical variables, are compared in their closeness to a corresponding conditional measure for an underlying jointly continuous distribution. Partial gamma and Kendall's partial tau are observed to be poorer than a weighted average of the Kendall's τ b 's from separate control levels, according to this criterion. The asymptotic…

有序分类数据偏关联度量的若干考量AI 解读

Some of the measures commonly used to describe association between two ordinal categorical variables, controlling for the effects of other ordinal categorical variables, are compared in their closeness to a corresponding conditional measure for an underlying jointly continuous distribution. Partial gamma and Kendall's partial tau are observed to be poorer than a weighted average of the Kendall's τ b 's from separate control levels, according to this criterion. The asymptotic…