A Fast Algorithm for Using Semi-Parametric Random Effects Model for Analyzing Longitudinal Data
AbstractMixed effects models are frequently used for analyzing longitudinal data. Normality assumption of random effects distrbution is a routine assumption for these models, violation of which leads to model misspecifcation and misleading parameter estimates. We propose a semi-parametric approach using gradient function for random effect estimation. In this approach, we relax the normality assumption for random effects by estimating their distribution over a pre-specifed grid. Unknown parameters of the marginal model are estimated using maximum likelihood methods. Some simulation studies and analyzing of a real data set are performed for illustration of the proposed semi-parametric method.
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