activity
20082022
most citedMinimization of divergences on sets of signed measures

49 citations · 128 across the 31 of their papers we have counts for

collaborators

36 papers

math.ST2022

A Unifying Framework for Some Directed Distances in Statistics

Michel Broniatowski, Wolfgang Stummer

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

cs.IT2021★ 7 cited

A precise bare simulation approach to the minimization of some distances. Foundations

Michel Broniatowski, Wolfgang Stummer

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

cs.IT2021★ 1 cited

Continuous indetermination and average likelihood minimization

Pierre Bertrand, Michel Broniatowski, Jean-François Marcotorchino

The authors transpose a discrete notion of indetermination coupling in the case of continuous probabilities. They show that this coupling, expressed on densities, cannot be capture…

cs.DM2020★ 4 cited

A constructive method to minimize couple matchings

Pierre Bertrand, Michel Broniatowski, Jean-François Marcotorchino

This paper provides constructive procedures for the indeterminacy coupling between two marginal distributions, an alternative to independence coupling. It also introduces a drawing…

math.ST2020★ 5 cited

Minimum divergence estimators, Maximum Likelihood and the generalized bootstrap

Michel Broniatowski

This paper is an attempt to set a justification for making use of some dicrepancy indexes, starting from the classical Maximum Likelihood definition, and adapting the corresponding…

cs.DM2020

Independence versus Indetermination: basis of two canonical clustering criteria

Pierre Bertrand, Michel Broniatowski, Jean-François Marcotorchino

This paper aims at comparing two coupling approaches as basic layers for building clustering criteria, suited for modularizing and clustering very large networks. We briefly use "o…