Semiparametric Ridge Regression for Longitudinal Data

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Taavoni Mozhagn; Arashi Mohammad

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Journal Paper

Paper Information

Journal: Journal of Data Science and Modeling Year:2022 | Volume:1 | Issue:1 Page(s): 159-170

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Information Journal Paper

Title

Semiparametric Ridge Regression for Longitudinal Data

Author(s)

Taavoni Mozhagn | Arashi Mohammad | Issue Writer Certificate

Keywords

Kernel

Longitudinal Data

Mixed Effect

Ridge Regression

Semiparametric

Abstract

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.

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