Robust maximum L9-likelihood estimation of joint mean-covariance models for longitudinal data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:171

Article

Xu, Lin¹  Xiang, Sijia¹  Yao, Weixin² 

[1] Zhejiang Univ Finance & Econ, Sch Data Sci, Hangzhou, Zhejiang, Peoples R China

[2] Univ Calif Riverside, Dept Stat, Riverside, CA 92521 USA

关键词: Joint mean-covariance models;    Longitudinal daaAlta analysis;    Maximum L-g-likelihood;    Modified Cholesky decomposition;   

DOI  :  10.1016/j.jmva.2019.01.001

来源: Elsevier

【 摘 要 】

A comprehensive longitudinal data analysis requires screening for unusual observations. Outliers or measurement errors might lead to considerable efficiency loss or even misleading results in longitudinal data inference. Via joint mean-covariance modelings (Pourahmadi, 2000; Zhang et al., 2015) and q-order entropy theory (Ferrari, 2010), we propose a maximum L-q-likelihood estimation for longitudinal data, which can yield robust and consistent estimators of the mean regression coefficients. An EM type algorithm is introduced to achieve both efficient and stable computation. The asymptotic properties of the proposed estimators are provided. Simulation studies and an application to Turkish anesthesiology data are used to show the effectiveness of the new approach. (C) 2019 Elsevier Inc. All rights reserved.

【 授权许可】

Free