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:: Volume 14, Issue 2 (2-2021) ::
J. of Stat. Sci. 2021, 14(2): 367-388 Back to browse issues page
Variable Selection in Semiparametric Mixed Effect Model for High-Dimension Longitudinal Data
Mozhgan Taavoni , Mohammad Arashi
Abstract:   (658 Views)
This paper considers the problem of simultaneous variable selection and estimation in a semiparametric mixed-effects model for longitudinal data with normal errors. We approximate the nonparametric function by regression spline and simultaneously estimate and select the variables under the optimization of the penalized objective function. Under some regularity conditions, the asymptotic behaviour of the resulting estimators is established in a high-dimensional framework where the number of parametric covariates increases as the sample size increases. For practical implementation, we use an EM algorithm to selects the significant variables and estimates the nonzero coefficient functions. Simulation studies are carried out to assess the performance of our proposed method, and a real data set is analyzed to illustrate the proposed procedure. 
Keywords: Longitudinal Data, Penalized Estimator, Smoothing Spline, Semiparametric Mixed Model, Variable Selection, HIV.
Full-Text [PDF 291 kb]   (76 Downloads)    
Type of Study: Applied | Subject: Statistical Inference
Received: 2019/09/20 | Accepted: 2020/04/11 | Published: 2021/02/28
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Taavoni M, Arashi M. Variable Selection in Semiparametric Mixed Effect Model for High-Dimension Longitudinal Data. J. of Stat. Sci.. 2021; 14 (2) :367-388
URL: http://jss.irstat.ir/article-1-678-en.html

Volume 14, Issue 2 (2-2021) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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