On information projections between multivariate elliptical and location-scale families

Abstract

We study information projections with respect to statistical $f$-divergences between any two location-scale families. We consider a multivariate generalization of the location-scale families which includes the elliptical and the spherical subfamilies. By using the action of the multivariate location-scale group, we show how to reduce the calculation of $f$-divergences between any two location-scale densities to canonical settings involving standard densities, and derive thereof fast Monte Carlo estimators of $f$-divergences with good properties. Finally, we prove that the minimum $f$-divergence between a prescribed density of a location-scale family and another location-scale family is independent of the prescribed location-scale parameter. We interpret geometrically this property.

23 pages, 2 figures

References in corpus (1)

• Hilbert geometry of the Siegel disk: The Siegel-Klein disk model

Cited by in corpus (1)

• On $f$-divergences between Cauchy distributions