LOG-GAMMA REGRESSION MODEL FOR ORDERED CATEGORICAL RESPONSE DATA

Regression with an ordered categorical response.

A survey on Mseleni joint disease in South Africa involved the scoring of pelvic X-rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading ...

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 ...

Bayesian paradigm for analysing count data in longitudina studies using Poisson-generalized log-gamma model

In analyzing longitudinal data with counted responses, normal distribution is usually used for distribution of the random efffects. However, in some applications random effects may not be normally distributed. Misspecification of this distribution may cause reduction of efficiency of estimators. In this paper, a generalized log-gamma distribution is used for the random effects which includes th...

Elastic Net Constrained Stereotype Logit Model for Ordered Categorical Data

In biomedical studies the outcome of interest may be an ordinal, rather than a dichotomous, class label such as progression of disease. An example of an ordinal variable is drug toxicity levels evaluated as mild, moderate or severe. Another example is the Breast Imaging Reporting and Data System (BIRADS) [1] classification system. After a mammogram is read, a subjective score is assigned based ...

Adaptive Tests for Ordered Categorical Data