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
DOI: https://doi.org/10.19139/soic.v2i4.38

Abstract

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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Published
2014-11-21
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
Issue
Vol 2 No 4 (2014)
Section
Research Articles
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