Latent-class trajectory modeling with a heterogeneous mean-variance relation
Den Teuling,Niek G. P. Ungolo,Francesco Pauws,Steffen C. van den Heuvel,Teuling Edwin R.
Den Teuling,Niek G. P.
Ungolo,Francesco
Pauws,Steffen C.
van den Heuvel,Teuling Edwin R.
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.
Description
Date
2025-10
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Volume Title
Publisher
Research Projects
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Keywords
Bayesian inference, Heteroskedasticity, Latent-class trajectory modeling, Mean-variance modeling, Mixture modeling
Citation
Den Teuling, N G P, Ungolo, F, Pauws, S C & van den Heuvel, T E R 2025, 'Latent-class trajectory modeling with a heterogeneous mean-variance relation', Computational Statistics & Data Analysis, vol. 210, 108199. https://doi.org/10.1016/j.csda.2025.108199