10.1007/s40096-018-0270-4

Improved mixed model for longitudinal data analysis using shrinkage method

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  • M. Rahmani1,
  • M. Arashi*1,
  • N. Mamode Khan2,
  • Y. Sunecher3,
  1. Shahrood University of Technology, Shahrood, IR
  2. University of Mauritius, Reduit, MU
  3. University of Technology Mauritius, Pointe-Aux-Sables, MU
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Published in Issue 2018-11-01

How to Cite

Rahmani, M., Arashi, M., Mamode Khan, N., & Sunecher, Y. (2018). Improved mixed model for longitudinal data analysis using shrinkage method. Mathematical Sciences, 12(4 (December 2018). https://doi.org/10.1007/s40096-018-0270-4
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Abstract

Abstract The problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis. In this context, this paper proposes a mixed ridge regression model via shrinkage methods to analyze such data. Furthermore, in view of obtaining more efficient estimators, we propose preliminary and Stein-type estimators using prior information for fixed-effects parameters. The model parameters are estimated via the EM algorithm. A simulation study is also presented to assess the performance of the estimators under different estimation methods. An application to the HIV data is also illustrated.

Keywords

  • EM algorithm,
  • Longitudinal data,
  • Mixed model,
  • Preliminary test,
  • Stein estimation,
  • Ridge regression
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