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5 papers · 2 filters
The maximizing set of the asymptotic normalized log-likelihood for partially observed Markov chains
Randal Douc, Francois Roueff, Tepmony Sim
This paper deals with a parametrized family of partially observed bivariate Markov chains. We establish that, under very mild assumptions, the limit of the normalized log-likelihoo…
Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm
Alain Durmus, Eric Moulines
In this paper, we study a method to sample from a target distribution over having a positive density with respect to the Lebesgue measure, known up to a normalis…
Handy sufficient conditions for the convergence of the maximum likelihood estimator in observation-driven models
Randal Douc, François Roueff, Tepmony Sim
This paper generalizes asymptotic properties obtained in the observation-driven times series models considered by \cite{dou:kou:mou:2013} in the sense that the conditional law of e…
Learning the dependence structure of rare events: a non-asymptotic study
Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Assessing the probability of occurrence of extreme events is a crucial issue in various fields like finance, insurance, telecommunication or environmental sciences. In a multivaria…
Low Rank Matrix Completion with Exponential Family Noise
Jean Lafond
The matrix completion problem consists in reconstructing a matrix from a sample of entries, possibly observed with noise. A popular class of estimator, known as nuclear norm penali…