An efficient and robust variable selection method for longitudinal generalized linear models

作者：Lv Jing^*; Yang Hu; Guo Chaohui

来源：Computational Statistics & Data Analysis, 2015, 82: 74-88.

DOI：10.1016/j.csda.2014.08.006

• Efficiency
• Generalized estimating equations
• Generalized linear model
• Longitudinal data
• Oracle property
• Robust estimation

This paper presents a new efficient and robust smooth-threshold generalized estimating equations for generalized linear models (GLMs) with longitudinal data. The proposed method is based on a bounded exponential score function and leverage-based weights to achieve robustness against outliers both in the response and the covariate domain. Our motivation for the new variable selection procedure is that it enables us to achieve better robustness and efficiency by introducing an additional tuning parameter gamma which can be automatically selected using the observed data. Moreover, its performance is near optimal and superior to some recently developed variable selection methods. Under some regularity conditions, the resulting estimator possesses the consistency in variable selection and the oracle property in estimation. Finally, simulation studies and a detailed real data analysis are carried out to assess and illustrate the finite sample performance, which show that the proposed method works better than other existing methods, in particular, when many outliers are included.

• 出版日期2015-2
• 单位重庆大学