Automatic Variable Selection for High-Dimensional Linear Models with Longitudinal Data

• 作者: Liugen Xue Ruiqin Tian
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Variable Selection Diverging Number of Parameters Longitudinal Data QUADRATIC INFERENCE FUNCTIONS GENERALIZED ESTIMATING Equation
• 年，卷（期）: 2014,（1）
• 页码范围: 38-48
• 页数: 11页
• 分类号: R73

摘要

High-dimensional longitudinal data arise frequently in biomedical and genomic research. It is important to select relevant covariates when the dimension of the parameters diverges as the sample size increases. We consider the problem of variable selection in high-dimensional linear models with longitudinal data. A new variable selection procedure is proposed using the smooth-threshold generalized estimating equation and quadratic inference functions (SGEE-QIF) to incorporate correlation information. The proposed procedure automatically eliminates inactive predictors by setting the corresponding parameters to be zero, and simultaneously estimates the nonzero regression coefficients by solving the SGEE-QIF. The proposed procedure avoids the convex optimization problem and is flexible and easy to implement. We establish the asymptotic properties in a high-dimensional framework where the number of covariates increases as the number of cluster increases. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure.