A generalized transition model for grouped longitudinal categorical data. Issue 8 (6th July 2020)

• Record Type: Journal Article
• Authors: Lara, Idemauro A. R.; Moral, Rafael A.; Taconeli, Cesar A.; Reigada, Carolina; Hinde, John
• Is Part Of: Biometrical journal. Volume 62:Issue 8(2020:Dec.)
• Page Start: 1837
• Page End: 1858
• Publication Date: 2020-07-06
• Subjects: discrete stochastic process -- generalized logit models -- multinomial distribution -- tests for stationarity; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900394
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Transition models are an important framework that can be used to model longitudinal categorical data. They are particularly useful when the primary interest is in prediction. The available methods for this class of models are suitable for the cases in which responses are recorded individually over time. However, in many areas, it is common for categorical data to be recorded as groups, that is, different categories with a number of individuals in each. As motivation we consider a study in insect movement and another in pig behaviou. The first study was developed to understand the movement patterns of female adults of Diaphorina citri, a pest of citrus plantations. The second study investigated how hogs behaved under the influence of environmental enrichment. In both studies, the number of individuals in different response categories was observed over time. We propose a new framework for considering the time dependence in the linear predictor of a generalized logit transition model using a quantitative response, corresponding to the number of individuals in each category. We use maximum likelihood estimation and present the results of the fitted models under stationarity and non‐stationarity assumptions, and use recently proposed tests to assess non‐stationarity. We evaluated the performance of the proposed model using simulation studies under different scenarios, and concluded that our modeling framework represents a flexible alternative to analyze grouped longitudinal categorical data.