Semiparametric Estimation of Covariance Matrixes for Longitudinal Data

• Published in: Journal of the American Statistical Association Vol. 103; no. 484; pp. 1520 - 1533
• Main Authors: Fan, Jianqing, Wu, Yichao
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.12.2008; American Statistical Association; Taylor & Francis Ltd
• Subjects: Applications; Asymptotic methods; Asymptotic properties; Context; Correlation analysis; Correlation structure; Correlations; Covariance; Covariance matrices; Covariance matrix; Data points; Difference-based estimation; Estimation bias; Estimation methods; Estimators; Exact sciences and technology; General topics; Linear inference, regression; Linear models; Longitudinal data; Mathematics; Matrix; Maximum likelihood estimators; Mean; Multivariate analysis; Normality; Parameter estimation; Parameters; Parametric models; Probability and statistics; Quasi-maximum likelihood; Regression analysis; Regression models; Robustness (mathematics); Sciences and techniques of general use; Simulation; Standard deviation; Statistical methods; Statistical variance; Statistics; Theory and Methods; Variance analysis; Varying-coefficient partially linear model; Correlation; Conditional distribution; Variance function; Statistical distribution; Variance estimation; Estimator robustness; Non parametric estimation; Multivariate analysis; Statistical simulation; Asymptotic convergence; Linear model; Variance; Semiparametric model; Covariance; Difference-based estimation; Regression function; Correlation function; Data structure; Covariance analysis; Linear regression; Asymptotic behavior; Statistical association; Covariance matrix; Semiparametric method; Variance analysis; Mean estimation; Statistical method; Statistical regression; Regression coefficient; Correlation analysis; Regression model; Asymptotic normality; Quasi-maximum likelihood: Varying-coefficient partially linear model; Quasi likelihood; Correlation structure; Maximum likelihood; Application; Biased estimation

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 estima...