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    A generalized transition model for grouped longitudinal categorical data


    Lara, Idemauro A. R. and de Andrade Moral, Rafael and Taconeli, Cesar A. and Reigada, Carolina and Hinde, John (2020) A generalized transition model for grouped longitudinal categorical data. Biometrical Journal, 62 (8). pp. 1837-1858. ISSN 0323-3847

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    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.

    Item Type: Article
    Additional Information: Cite as: Lara, IAR, Moral, RA, Taconeli, CA, Reigada, C, Hinde, J. A generalized transition model for grouped longitudinal categorical data. Biometrical Journal. 2020; 62: 1837– 1858. https://doi.org/10.1002/bimj.201900394
    Keywords: discrete stochastic process; generalized logit models; multinomial distribution; tests for stationarity;
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15553
    Identification Number: https://doi.org/10.1002/bimj.201900394
    Depositing User: Rafael de Andrade Moral
    Date Deposited: 22 Feb 2022 15:50
    Journal or Publication Title: Biometrical Journal
    Publisher: Wiley
    Refereed: Yes
    URI:

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