Generalized linear models for ordered categorical data

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 ...

Linear Models for Functional Data

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oglmx: A Package for Estimation of Ordered Generalized Linear Models

Ordered discrete dependent variable models such as ordered probit and ordered logit are frequently used across the social sciences to study outcomes including health status, happiness, wealth and educational attainment. Unlike in the case of OLS, unaccounted for heteroskedasticity in these models can lead to biased parameter estimates. This paper introduces the oglmx package for the R statistic...

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Adaptive Tests for Ordered Categorical Data