Finite mixture models have offered a very important tool for exploring complex data
structures in many scientific areas, such as economics, epidemiology and finance …

In this paper, we propose a new effective estimator for a class of semiparametric mixture
models where one component has known distribution with possibly unknown parameters …

We study a two-component semiparametric mixture model where one component
distribution belongs to a parametric class, while the other is symmetric but otherwise …

We consider a two-component location mixture model with symmetric components, one of
which is assumed to be known, the other is unknown. We show identifiability under …

We propose the novel concept of anomaly-free regions (AFR) to improve anomaly detection.
An AFR is a region in the data space for which it is known that there are no anomalies inside …

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 …

Mixture models are a powerful tool for analyzing complex and heterogeneous datasets
across many scientific fields, from finance to genomics. Mixture Models: Parametric …

In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of
semiparametric mixture models where one component has known distribution with possibly …

Estimation of parametric and semiparametric mixture models using phi-divergences Page 1
HAL Id: tel-01452799 https://tel.archives-ouvertes.fr/tel-01452799 Submitted on 2 Feb 2017 …

Estimation of a two-component mixture model with an unknown component is very difficult
when no particular assumption is made on the structure of the unknown component. A …