Modelling covariance structure in bivariate marginal models for longitudinal data

• 成果类型: Article
• 署名作者: Xu, Jing; Mackenzie, Gilbert
• 署名单位: University of London; University of Limerick
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass031
• 发表日期: 2012
• 页码: 649662
• 关键词: selection; matrices

摘要

It can be more challenging to efficiently model the covariance matrices for multivariate longitudinal data than for the univariate case, due to the correlations arising between multiple responses. The positive-definiteness constraint and the high dimensionality are further obstacles in covariance modelling. In this paper, we develop a data-based method by which the parameters in the covariance matrices are replaced by unconstrained and interpretable parameters with reduced dimensions. The maximum likelihood estimators for the mean and covariance parameters are shown to be consistent and asymptotically normally distributed. Simulations and real data analysis show that the new approach performs very well even when modelling bivariate nonstationary dependence structures.