Title : ( High-dimensional semiparametric mixed-effects model for longitudinal data with non-normal errors )
Authors: mozhgan taavoni , Mohammad Arashi ,Access to full-text not allowed by authors
Abstract
Difficulties may arise when analyzing longitudinal data using mixed-effects models if nonparametric functions are present in the linear predictor component. This study extends semiparametric mixed-effects modeling in cases when the response variable does not always follow a normal distribution and the nonparametric component is structured as an additive model. A novel approach is proposed to identify significant linear and non-linear components using a double-penalized generalized estimating equation with two penalty terms. Furthermore, the iterative approach intends to enhance the efficiency of estimating regression coefficients by incorporating the calculation of the working covariance matrix. The oracle properties of the resulting estimators are established under certain regularity conditions, where the dimensions of both the parametric and nonparametric components increase as the sample size grows. We perform numerical studies to demonstrate the efficacy of our proposal.
Keywords
, GEE, longitudinal data, non-normal errors, penalized likelihood, semiparametric mixed-effects model@article{paperid:1101379,
author = {Taavoni, Mozhgan and Arashi, Mohammad},
title = {High-dimensional semiparametric mixed-effects model for longitudinal data with non-normal errors},
journal = {Statistics},
year = {2025},
volume = {59},
number = {1},
month = {January},
issn = {0233-1888},
pages = {207--227},
numpages = {20},
keywords = {GEE; longitudinal data; non-normal errors; penalized likelihood; semiparametric mixed-effects model},
}
%0 Journal Article
%T High-dimensional semiparametric mixed-effects model for longitudinal data with non-normal errors
%A Taavoni, Mozhgan
%A Arashi, Mohammad
%J Statistics
%@ 0233-1888
%D 2025