【摘 要】
：
Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully par
【机 构】
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UniversityofTexasatAustin,UnitedStates
【出 处】
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The 24th International Workshop on Matrices and Statistics(第
论文部分内容阅读
Estimation of the covariance structure for irregular sparse longitudinal data has been studied by many authors in recent years but typically using fully parametric specifications. In addition, when data are collected from several groups over time, it is known that assuming the same or completely different covariance matrices over groups can lead to loss of efficiency and/or bias. Nonparametric approaches have been proposed for estimating the covariance matrix for regular univariate longitudinal data by sharing information across the groups under study. For the irregular case, with longitudinal measurements that are bivariate or multivariate, modeling becomes more difficult. In this talk, to model bivariate sparse longitudinal data from several groups, we propose a flexible covariance structure via a novel matrix stick-breaking processes.
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