Geometrically concave univariate distributions

In this paper our aim is to show that if a probability density function is geometrically concave (convex), then the corresponding cumulative distribution function and the survival function are geometrically concave (convex) too, under some assumptions. The proofs are based on the so-called monotone form of lʹHospitalʹs rule and permit us to extend our results to the case of the concavity (convexity) with respect to Hِlder means. To illustrate the applications of the main results, we discuss in details the geometrical concavity of the probability density function, cumulative distribution function and survival function of some common continuous univariate distributions. Moreover, at the end of the paper, we present a simple alternative proof to Schweizerʹs problem related to the Mulhollandʹs generalization of Minkowskiʹs inequality.