Resumen de Cholesky-based multivariate gaussian regression
Thomas Muschinski, Georg von Mayr, Thorsten Simon, Achim Zeileis
Multivariate Gaussian regression has applications in many elds, but is made dicult by the high model complexity and positive-de nite requirement on the estimated covariance. We implement multivariate Gaussian regression through a Cholesky-based reparameterization of the covariance matrix. The distributional parameters|the means and the entries of the Cholesky factor|can be made to depend on covariates through exible additive predictors, allowing for nonlinear variations in mean and covariance. The reparameterization is compared to reference methods for estimating a xed covariance. An application for weather prediction (surface temperature) illustrates the exibility of the approach.
