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Semiparametric Ridge Regression for Longitudinal Data | ||
| Journal of Data Science and Modeling | ||
| مقاله 11، دوره 1، شماره 1، اسفند 2022، صفحه 159-170 اصل مقاله (263.48 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jcsm.2019.36950.1015 | ||
| نویسنده | ||
| mozhgan taavoni* | ||
| Department of Statistic‎, Faculty of Mathematical Sciences, ‎Shahrood University of Technology‎, ‎Shahrood‎, ‎Iran | ||
| چکیده | ||
| This paper considers an extension of the linear mixed model, called semiparametric mixed effects model, for longitudinal data, when multicollinearity is present. To overcome this problem, a new mixed ridge estimator is proposed while the nonparametric function in the semiparametric model is approximated by the kernel method. The proposed approache integrates ridge method into the semiparametric mixed effects modeling framework in order to account for both the correlation induced by repeatedly measuring an outcome on each individual over time, as well as the potentially high degree of correlation among possible predictor variables. The asymptotic normality of the exhibited estimator is established. To improve efficiency, the estimation of the covariance function is accomplished using an iterative algorithm. Performance of the proposed estimator is compared through a simulation study and analysis of CD4 data. | ||
| کلیدواژهها | ||
| Kernel؛ Longitudinal Data؛ Mixed Effect؛ Ridge Regression؛ Semiparametric | ||
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آمار تعداد مشاهده مقاله: 526 تعداد دریافت فایل اصل مقاله: 957 |
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