Latent-class trajectory modeling with a heterogeneous mean-variance relation

Niek G.P. Den Teuling, Francesco Ungolo, Steffen C. Pauws and Edwin R. van den Heuvel

Computational Statistics & Data Analysis, 2025, vol. 210, issue C

Abstract: The benefit of addressing heteroskedastic residual variances across trajectories is investigated with the purpose of finding clusters of longitudinal trajectories. Models are proposed to account for class-specific heteroskedasticity through a mean-variance relation or random residual variance, thereby accounting for trajectory-specific variance. The analyzed latent-class trajectory models are an extension of growth mixture models (GMM). The estimation bias of the model parameters and the recoverability of the number of latent classes are assessed under various data-generating models and settings by means of a simulation study. Furthermore, the empirical applicability of these models is demonstrated through the analysis of the time-varying incidence rate of COVID-19 cases across counties in the United States. Overall, the class-specific mean-variance could be reliably estimated by the proposed models in datasets comprising 250 trajectories. In addition, the extended GMM accounting for the residual random variance showed improved group trajectory estimation over the standard GMM.

Keywords: Mixture modeling; Latent-class trajectory modeling; Heteroskedasticity; Mean-variance modeling; Bayesian inference (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:210:y:2025:i:c:s0167947325000751

DOI: 10.1016/j.csda.2025.108199

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