Work

Structure Identification, Estimation and Variable Selection for Varying Coefficient EV Models With Longitudinal Data

Year: 2026

Type: article

Abstract: In this article, we propose a bias-corrected double penalized quadratic inference functions method to simultaneously identify model structure, estimate parameters, and perform variable selection for varying coefficient errors-in-variables (EV) models with longitudinal data. Unlike the linear models or the partial linear varying coefficient models, the proposed method does not assume in advance whether each regression coefficient is constant or varying. Instead, it represents each coefficient as a nonparametric function and identifies whether it is constant or varying using the proposed method. By employing a B-spline basis to approximate the unknown coefficient functions, the proposed method integrates a bias-corrected quadratic inference function with two penalized terms to achieve structure identification, estimation, and variable selection. Under certain regularized conditions, the consistency and sparsity properties of the estimator are established. Moreover, a three-step iterative algorithm is developed to implement the proposed method in practice. Simulation studies and a real data analysis demonstrate the superior finite-sample performance of the method.

Source: Statistics in Medicine

DOI: 10.1002/sim.70434

Authors: Mingtao Zhao, Jingxiang Cao, Jun Sun, Yan Fan, Sanying Feng, Fanqun Li

Institutions: Anhui University of Finance and Economics, Shanghai University of International Business and Economics, Zhengzhou University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, University Natural Science Research Project of Anhui Province, National Social Science Fund of China