Abstract
The problem of multicollinearity among predictor variables is a frequent issue in longitudinal data analysis. In this context, this paper proposes a mixed ridge regression model via shrinkage methods to analyze such data. Furthermore, in view of obtaining more efficient estimators, we propose preliminary and Stein-type estimators using prior information for fixed-effects parameters. The model parameters are estimated via the EM algorithm. A simulation study is also presented to assess the performance of the estimators under different estimation methods. An application to the HIV data is also illustrated.
Highlights
In longitudinal data setup, repeated measures of some variables of interest are collected over a specified time period for different independent subjects or individuals
We develop the preliminary test and shrinkage estimation methods for the analysis of longitudinal data in ridge regression context, where some parameters are subject to certain/uncertain restrictions
A Monte Carlo simulation study is conducted to evaluate the performance of the proposed preliminary test MR (PTMR) and shrinkage estimators compared to the mixed ridge (MR) estimator of Eliot et al [13]
Summary
In longitudinal data setup, repeated measures of some variables of interest are collected over a specified time period for different independent subjects or individuals. Keywords EM algorithm · Longitudinal data · Mixed model · Preliminary test · Stein estimation · Ridge regression We develop the preliminary test and shrinkage estimation methods for the analysis of longitudinal data in ridge regression context, where some parameters are subject to certain/uncertain restrictions.
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