C
Catherine Matias
Researcher at Centre national de la recherche scientifique
Publications - 69
Citations - 2358
Catherine Matias is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Estimator & Identifiability. The author has an hindex of 21, co-authored 67 publications receiving 2023 citations. Previous affiliations of Catherine Matias include University of Paris & Paris Diderot University.
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Identifiability of parameters in latent structure models with many observed variables
TL;DR: A general approach for establishing identifiability utilizing algebraic arguments is demonstrated, which sheds light on the properties of finite mixtures of Bernoulli products, which have been used for decades despite being known to have nonidentifiable parameters.
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Identifiability of parameters in latent structure models with many observed variables
TL;DR: In this paper, a general approach for establishing identifiability of hidden class models utilizing algebraic arguments is presented. But this approach is restricted to a class of models, such as mixtures of both finite and nonparametric product distributions.
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Statistical clustering of temporal networks through a dynamic stochastic block model
Catherine Matias,Vincent Miele +1 more
TL;DR: This work explores statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time and proposes an inference procedure based on a variational expectation–maximization algorithm.
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Statistical clustering of temporal networks through a dynamic stochastic block model
Catherine Matias,Vincent Miele +1 more
TL;DR: In this paper, the authors explore statistical properties and frequentist inference in a model that combines a stochastic block model (SBM) for its static part with independent Markov chains for the evolution of the nodes groups through time.
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Asymptotics of the maximum likelihood estimator for general hidden Markov models
Randal Douc,Catherine Matias +1 more
TL;DR: In this article, the authors consider the consistency and asymptotic normality of the maximum likelihood estimator for a possibly non-stationary hidden Markov model where the hidden state space is a separable and compact space not necessarily finite, and both the transition kernel of the hidden chain and the conditional distribution of the observations depend on a parameter.