partially generalized ridge regression with longitudinal data

This paper considers the multicollinearity problem for longitudinal data inthe framework of generalized partially linear model (gplm). to overcome this problem,a new ridge estimation method is proposed while the nonparametric function inthe partially part is approximated using splines. the proposed approach integratesridge method into the generalized estimating equation method in order to account forboth the correlation induced by repeatedly measuring an outcome on each individualover time and the potentially high degree of correlation among possible predictorvariables. the asymptotic normality of the exhibited estimator is established. theperformance of the proposed estimator is assessed through numerical studies.