A Fast Algorithm for Using Semi-Parametric Random Effects Model for Analyzing Longitudinal Data

  • Taban Baghfalaki Department of Statistics Shahid Beheshti University Tehran, Iran
  • Mojtaba Ganjali Department of Statistics, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran


Mixed 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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How to Cite
Baghfalaki, T., & Ganjali, M. (2014). A Fast Algorithm for Using Semi-Parametric Random Effects Model for Analyzing Longitudinal Data. Statistics, Optimization & Information Computing, 2(4), 339-351. https://doi.org/10.19139/soic.v2i4.38
Research Articles