Multivariate log-concave distributions as a nearly parametric model

Schuhmacher Dominic, Hüsler André and Dümbgen Lutz
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Hüsler André: University of Bern, Institute of Mathematical Statistics and Actuarial, Bern, Schweiz
Dümbgen Lutz: University of Bern, Institute of Mathematical Statistics and Actuarial, Bern, Schweiz

Statistics & Risk Modeling, 2011, vol. 28, issue 3, 277-295

Abstract: In this paper we show that the family Pd(lc) of probability distributions on ℝd with log-concave densities satisfies a strong continuity condition. In particular, it turns out that weak convergence within this family entails (i) convergence in total variation distance, (ii) convergence of arbitrary moments, and (iii) pointwise convergence of Laplace transforms. In this and several other respects the nonparametric model Pd(lc) behaves like a parametric model such as, for instance, the family of all d-variate Gaussian distributions. As a consequence of the continuity result, we prove the existence of nontrivial confidence sets for the moments of an unknown distribution in Pd(lc). Our results are based on various new inequalities for log-concave distributions which are of independent interest.

Keywords: confidence set; moments; Laplace transform; total variation; weak continuity (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1524/stnd.2011.1073

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