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Paper Information

Journal:   MATHEMATICAL SCIENCES   December 2018 , Volume 12 , Number 4; Page(s) 305 To 312.

Improved mixed model for longitudinal data analysis using shrinkage method

Author(s):  RAHMANI M., ARASHI M.*, Mamode Khan n., Sunecher y.
* Shahrood University of Technology, Shahrood, Iran
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 fixedeffects 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.
Keyword(s): EM algorithm,Longitudinal data,Mixed model,Preliminary test,Stein estimation,Ridge regression
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