Variable Selection in Generalized Semiparametric Longitudinal Models

The process of selecting variables for predictive generalized longitudinal model is difficult due to potential correlations among observations and the true distribution of the response variable. Estimating regression coefficients using Lasso regression is a straightforward process, provided that we accurately determine the linear predictor component and the distribution of the response variable. We employ an efficient selection mechanism within the flexible generalized semiparametric longitudinal model, where we consider the possibility of nonlinear dependence between predictors and the response variable. In our application, we focus on on the selection of variables in the regression modeling CD4 levels in the Human Immunodeficiency Virus (HIV) positive patients. It is revealed that interaction effects must be taken into account in regression modeling CD4 counts.