Empirical Likelihood Based Longitudinal Data Analysis

• 作者: Asokan Mulayath Variyath J Concepción Loredo-Osti Tharshanna Nadarajah
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: Longitudinal Data Generalized Estimating Equations Empirical Likelihood Adjusted Empirical Likelihood Extended Empirical Likelihood
• 年，卷（期）: 2020,（4）
• 页码范围: 611-639
• 页数: 29页
• 分类号: O17

摘要

In longitudinal data analysis, our primary interest is in the estimation of regression parameters for the marginal expectations of the longitudinal responses, and the longitudinal correlation parameters are of secondary interest. The joint likelihood function for longitudinal data is challenging, particularly due to correlated responses. Marginal models, such as generalized estimating equations (GEEs), have received much attention based on the assumption of the first two moments of the data and a working correlation structure. The confidence regions and hypothesis tests are constructed based on the asymptotic normality. This approach is sensitive to the misspecification of the variance function and the working correlation structure which may yield inefficient and inconsistent estimates leading to wrong conclusions. To overcome this problem, we propose an empirical likelihood (EL) procedure based on a set of estimating equations for the parameter of interest and discuss its <span style="font-family:Verdana;">characteristics and asymptotic properties. We also provide an algorithm base</span><span style="font-family:Verdana;">d on EL principles for the estimation of the regression parameters and the construction of its confidence region. We have applied the proposed method in two case examples.</span>