41 citations · 214 across the 27 of their papers we have counts for
22 papers · 1 filter
Deconvolution of repeated measurements corrupted by unknown noise
Jérémie Capitao-Miniconi, Elisabeth Gassiat, Luc Lehéricy
Recent advances have demonstrated the possibility of solving the deconvolution problem without prior knowledge of the noise distribution. In this paper, we study the repeated measu…
Frontiers to the learning of nonparametric hidden Markov models
Kweku Abraham, Elisabeth Gassiat, Zacharie Naulet
Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the population densities. Identifi…
Support and distribution inference from noisy data
Jérémie Capitao-Miniconi, Elisabeth Gassiat, Luc Lehéricy
We consider noisy observations of a distribution with unknown support. In the deconvolution model, it has been proved recently [19] that, under very mild assumptions, it is possibl…
Deconvolution of spherical data corrupted with unknown noise
Jérémie Capitao-Miniconi, Elisabeth Gassiat
We consider the deconvolution problem for densities supported on a -dimensional sphere with unknown center and unknown radius, in the situation where the distribution of the…
Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach
Kweku Abraham, Ismael Castillo, Elisabeth Gassiat
Given a nonparametric Hidden Markov Model (HMM) with two states, the question of constructing efficient multiple testing procedures is considered, treating one of the states as an…
Deconvolution with unknown noise distribution is possible for multivariate signals
Elisabeth Gassiat, Sylvain Le Corff, Luc Lehéricy
This paper considers the deconvolution problem in the case where the target signal is multidimensional and no information is known about the noise distribution. More precisely, no…