Kernel estimation in semiparametric mixed effect longitudinal modeling

• Published in: Statistical papers (Berlin, Germany) Vol. 62; no. 3; pp. 1095 - 1116
• Main Authors: Taavoni, M., Arashi, M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Asymptotic methods; Asymptotic properties; Computer simulation; Covariance; Economic models; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Finance; HIV; Human immunodeficiency virus; Immune system; Insurance; Iterative algorithms; Iterative methods; Kernels; Management; Mathematics and Statistics; Modelling; Normality; Operations Research/Decision Theory; Probability Theory and Stochastic Processes; Regression analysis; Regression coefficients; Regular Article; Statistics; Statistics for Business; Variance; Backfitting; CD4 data; Asymptotic distribution; Longitudinal data; Kernel; Semiparametric mixed effects model

HIV damages the immune system by targeting the CD4 cells. Hence CD4 count data modeling is important in the analysis of HIV infection. This paper considers a semiparametric mixed effects model for the analysis of CD4 longitudinal data. The model is a natural extension to the linear mixed and semipar...