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Latent Mixture Quantile Regression for Longitudinal Data

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This paper proposes mixture median and quantile models for describing latent growth curves of longitudinal outcomes. The models exhibit the different latent classes of evolution of the underlying outcome process. The mixture median model can be used as a robust alternative to the Gaussian likelihood based latent class model for skewed data, and the quantile models provide a complete regression picture for investigating the latent class structure at different quantiles. The within-subject correlation is incorporated by a marginal approach based on the idea of weighting. The weighted estimating equations for the model parameters are given, and a penalized weighted loss function is defined to select the optimal number of latent classes.

This talk is part of the MRC Biostatistics Unit Seminars series.

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