Multicollinearity and linear predictor link function problems in regression modelling of longitudinal data

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Authors

Taavoni, Mozhgan
Arashi, Mohammad
Manda, S.O.M. (Samuel)

Journal Title

Journal ISSN

Volume Title

Publisher

MDPI

Abstract

In the longitudinal data analysis we integrate flexible linear predictor link function and highcorrelated 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.

Description

DATA AVAILABILITY STATEMENT: The data is publicly available.

Keywords

Generalized estimating equations, Longitudinal data, Multicollinearity, Partially generalized linear models, Ridge regression

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Citation

Taavoni, M.; Arashi, M.; Manda, S. Multicollinearity and Linear Predictor Link Function Problems in Regression Modelling of Longitudinal Data. Mathematics 2023, 11, 530. https://doi.org/10.3390/math11030530.