Semiparametric Estimation of Covariance Matrixes for Longitudinal Data

• 成果类型: Article
• 署名作者: Fan, Jianqing; Wu, Yichao
• 署名单位: Princeton University; Shanghai University of Finance & Economics; North Carolina State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000742
• 发表日期: 2008
• 页码: 1520-1533
• 关键词: regression; variance; inference; models

摘要

Estimation of longitudinal data covariance structure Poses significant challenges because the data usually are collected at irregular time points. A viable semiparametric model for covariance matrixes has been proposed that allows one to estimate the variance function nonparametrically and to estimate the correlation function parametrically by aggregating information front irregular and sparse data points within each subject. But the asymptotic properties of the quasi-maximum likelihood estimator (QMLE) of parameters in the covariance model are largely, unknown. We address this problem in the context of more general models for the conditional (QMLE) of parameters in the covariance. including parametric, nonparametric, or semiparametric. We also consider the possibility of rough mean regression function and introduce the difference-based method to reduce biases in the context of varying-coefficient partially linear mean regression Models. This provides a more robust estimator of the covariance function under a wider range of situation,. Under some technical conditions. consistency and asymptotic normality are obtained for the QMLE of of the Parameters ill the correlation function. Simulation Studies and a real data example are used to illustrate the Proposed approach.