A Generalized Relative (α, β)-Entropy: Geometric Properties and Applications to Robust Statistical Inference

Abhik Ghosh, Ayanendranath Basu
2018 Entropy  
Entropy and relative entropy measures play a crucial role in mathematical information theory. The relative entropies are also widely used in statistics under the name of divergence measures which link these two fields of science through the minimum divergence principle. Divergence measures are popular among statisticians as many of the corresponding minimum divergence methods lead to robust inference in the presence of outliers in the observed data; examples include the ϕ -divergence, the
more » ... vergence, the density power divergence, the logarithmic density power divergence and the recently developed family of logarithmic super divergence (LSD). In this paper, we will present an alternative information theoretic formulation of the LSD measures as a two-parameter generalization of the relative α -entropy, which we refer to as the general ( α , β ) -entropy. We explore its relation with various other entropies and divergences, which also generates a two-parameter extension of Renyi entropy measure as a by-product. This paper is primarily focused on the geometric properties of the relative ( α , β ) -entropy or the LSD measures; we prove their continuity and convexity in both the arguments along with an extended Pythagorean relation under a power-transformation of the domain space. We also derive a set of sufficient conditions under which the forward and the reverse projections of the relative ( α , β ) -entropy exist and are unique. Finally, we briefly discuss the potential applications of the relative ( α , β ) -entropy or the LSD measures in statistical inference, in particular, for robust parameter estimation and hypothesis testing. Our results on the reverse projection of the relative ( α , β ) -entropy establish, for the first time, the existence and uniqueness of the minimum LSD estimators. Numerical illustrations are also provided for the problem of estimating the binomial parameter.
doi:10.3390/e20050347 pmid:33265437 fatcat:sfohtua5prdvvhkud32c7wyahy