A Nonparametric Prior for Simultaneous Covariance Estimation.

ABSTRACT: In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge n

SUBMITTER: Gaskins JT 

PROVIDER: S-EPMC3852937 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature