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Paper Information

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

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
 
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 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
 
 
References: 
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Citations: 
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+ Click to Cite.
APA: Copy

RAHMANI, M., & ARASHI, M., & Mamode Khan, n., & Sunecher, y. (2018). Improved mixed model for longitudinal data analysis using shrinkage method. MATHEMATICAL SCIENCES, 12(4), 305-312. https://www.sid.ir/en/journal/ViewPaper.aspx?id=668455



Vancouver: Copy

RAHMANI M., ARASHI M., Mamode Khan n., Sunecher y.. Improved mixed model for longitudinal data analysis using shrinkage method. MATHEMATICAL SCIENCES. 2018 [cited 2021June16];12(4):305-312. Available from: https://www.sid.ir/en/journal/ViewPaper.aspx?id=668455



IEEE: Copy

RAHMANI, M., ARASHI, M., Mamode Khan, n., Sunecher, y., 2018. Improved mixed model for longitudinal data analysis using shrinkage method. MATHEMATICAL SCIENCES, [online] 12(4), pp.305-312. Available: https://www.sid.ir/en/journal/ViewPaper.aspx?id=668455.



 
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