Multicollinearity and linear predictor link function problems in regression modelling of longitudinal data
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Date
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
Sustainable Development Goals
None
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.