Individual-specific, sparse inverse covariance estimation in generalized estimating equations

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• 作者：Qiang Zhang^a ; Edward H. Ip^a ; ^{eip@wakehealth.edu} ; Junhao Pan^b ; Robert Plemmons^c
• 关键词：Orthogonal matching pursuit ; Covariance modeling ; Asymptotic
• 刊名：Statistics & Probability Letters
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：122
• 期：Complete
• 页码：96-103
• 全文大小：508 K
• 卷排序：122

文摘

This paper proposes a data-driven approach that derives individual-specific sparse working correlation matrices for generalized estimating equations (GEEs). The approach is motivated by the observation that, in some applications of the GEE, the covariance structure across individuals is heterogeneous and cannot be appropriately captured by a single correlation matrix. The proposed approach enjoys both favorable computational and asymptotic properties. Simulation experiments and analysis of intensively measured longitudinal data on 158 participants collected from a dietary and emotion study are presented.