The Matrix-F Prior for Estimating and Testing Covariance Matrices ---- Joris Mulder
- https://wsc.project.cwi.nl/ml-reading-group/events/the-matrix-f-prior-for-estimating-and-testing-covariance-matrices-joris-mulder
- The Matrix-F Prior for Estimating and Testing Covariance Matrices ---- Joris Mulder
- 2018-04-05T11:00:00+02:00
- 2018-04-05T12:00:00+02:00
- When Apr 05, 2018 from 11:00 AM to 12:00 PM (Europe/Amsterdam / UTC200)
- Where L017
- Add event to calendar iCal
The matrix-F distribution is presented as prior for covariance matrices as an alternative to the conjugate inverted Wishart distribution. A special case of the univariate FF distribution for a variance parameter is equivalent to a half-t distribution for a standard deviation, which is becoming increasingly popular in the Bayesian literature. The matrix-F distribution can be conveniently modeled as a Wishart mixture of Wishart or inverse Wishart distributions, which allows straightforward implementation in a Gibbs sampler. By mixing the covariance matrix of a multivariate normal distribution with a matrix-F distribution, a multivariate horseshoe type prior is obtained which is useful for modeling sparse signals. Furthermore, it is shown that the intrinsic prior for testing covariance matrices in non-hierarchical models has a matrix-F distribution. This intrinsic prior is also useful for testing inequality constrained hypotheses on variances. Finally through simulation it is shown that the matrix-variate F distribution has good frequentist properties as prior for the random effects covariance matrix in generalized linear mixed models.