Covariance Partition Priors: A Bayesian Approach to Simultaneous Covariance Estimation for Longitudinal Data. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: Gaskins, J. T.; Daniels, M. J.
• Is Part Of: Journal of computational and graphical statistics. Volume 25:Issue 1(2016)
• Page Start: 167
• Page End: 186
• Publication Date: 2016-01-02
• Subjects: Cholesky parameterization -- Clustering -- Markov chains -- Shrinkage -- Sparsity; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2015.1028549
• Languages: English
• ISSNs: 1061-8600

Abstract

Abstract : The estimation of the covariance matrix is a key concern in the analysis of longitudinal data. When data consist of multiple groups, it is often assumed the covariance matrices are either equal across groups or are completely distinct. We seek methodology to allow borrowing of strength across potentially similar groups to improve estimation. To that end, we introduce a covariance partition prior that proposes a partition of the groups at each measurement time. Groups in the same set of the partition share dependence parameters for the distribution of the current measurement given the preceding ones, and the sequence of partitions is modeled as a Markov chain to encourage similar structure at nearby measurement times. This approach additionally encourages a lower-dimensional structure of the covariance matrices by shrinking the parameters of the Cholesky decomposition toward zero. We demonstrate the performance of our model through two simulation studies and the analysis of data from a depression study. This article includes Supplementary Materials available online.