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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