Density-based directed distances—particularly known as divergences—between probability
distributions are widely used in statistics as well as in the adjacent research fields of …

In information theory—as well as in the adjacent fields of statistics, machine learning,
artificial intelligence, signal processing and pattern recognition—many flexibilizations of the …

L-moments are expected values of linear combinations of order statistics that provide robust
alternatives to traditional moments. The estimation of parametric models by matching …

We propose a structure of a semiparametric two-component mixture model when one
component is parametric and the other is defined through linear constraints on either its …

Entropy | Free Full-Text | Minimum Divergence Estimators, Maximum Likelihood and the
Generalized Bootstrap Next Article in Journal Statistical Inference of Truncated Normal …

The constrained minimization (respectively maximization) of directed distances and of
related generalized entropies is a fundamental task in information theory as well as in the …

This paper studies information release in symmetric, independent private value auctions
with multiple objects and unit demand. We compare effects on welfare to those on the …

Sanov-type results hold for some weighted versions of empirical measures, and the rates for
those Large Deviation principles can be identified as divergences between measures, which …

This chapter reviews the basic features of statistical inference in parametric and semi‐
parametric models through duality techniques in the realm of divergence‐based methods. It …

The study of mixture models constitutes a large domain of research in statistics. In the first
part of this work, we present phi-divergences and the existing methods which produce …