Multicollinearity and Linear Predictor Link Function Problems in Regression Modelling of Longitudinal Data

Mozhgan Taavoni, Mohammad Arashi () and Samuel Manda
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Mozhgan Taavoni: Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Mohammad Arashi: Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran
Samuel Manda: Department of Statistics, Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria 0028, South Africa

Mathematics, 2023, vol. 11, issue 3, 1-9

Abstract: In the longitudinal data analysis we integrate flexible linear predictor link function and high-correlated predictor variables. Our approach uses B-splines for non-parametric part in the linear predictor component. A generalized estimation equation is used to estimate the parameters of the proposed model. We assess the performance of our proposed model using simulations and an application to an analysis of acquired immunodeficiency syndrome data set.

Keywords: generalized estimating equations; longitudinal data; multicollinearity; partially generalized linear models; ridge regression (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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