Nested Generalized Linear Model with Ordinal Response for Correlated Data

Modeling Paired Ordinal Response Data

 About 25 years ago, McCullagh proposed a method for modeling univariate ordinal responses. After publishing this paper, other statisticians gradually extended his method, such that we are now able to use more complicated but efficient methods to analyze correlated multivariate ordinal data, and model the relationship between these responses and host of covariates. In this paper, we aim to...

A Unified Bayesian Approach for Analyzing Correlated Ordinal Response Data

Generalized linear models with scale mixture of multivariate normal (SMMVN) link functions are considered to model correlated ordinal response data. A unified Bayesian approach, which includes prior elicitation and model comparison, is proposed. In order to incorporate available prior information, we propose a class of informative prior distributions on model parameters. The propriety of the pr...

A Generalized Linear Model for Repeated Ordered Categorical Response Data

We proposed a new approach to model longitudinal data consisting of transitional frequencies classified according to an ordered categorical response variable. Following an approach of Kalbfleisch and Lawless (1985), the responses are assumed to be sampled from an underlying continuous-time finite-state-space Markov chain, with the further assumption that direct transitions are strictly between ...

Graphical model checking with correlated response data.

Correlated response data arise often in biomedical studies. The generalized estimation equation (GEE) approach is widely used in regression analysis for such data. However, there are few methods available to check the adequacy of regression models in GEE. In this paper, a graphical method is proposed based on Cook and Weisberg's marginal model plot. A bootstrap method is applied to obtain the r...

Generalized Odds Ratios for Ordinal Data