69 citations · 186 across the 20 of their papers we have counts for
9 papers · 1 filter
Model selection for Markov random fields on graphs under a mixing condition
Florencia Leonardi, Magno T. F Severino
In this work, we propose a global model selection criterion to estimate the graph of conditional dependencies of a random vector based on a finite sample. By global criterion, we m…
Consistent model selection for the Degree Corrected Stochastic Blockmodel
Andressa Cerqueira, Sandro Gallo, Florencia Leonardi +1
The Degree Corrected Stochastic Block Model (DCSBM) was introduced by \cite{karrer2011stochastic} as a generalization of the stochastic block model in which vertices of the same co…
Strong consistency of Krichevsky-Trofimov estimator for the number of communities in the Stochastic Block Model
Andressa Cerqueira, Florencia Leonardi
In this paper we introduce an estimator for the number of communities in the Stochastic Block Model (SBM), based on the maximization of a penalized version of the so-called Krichev…
Nonparametric statistical inference for the context tree of a stationary ergodic process
Sandro Gallo, Florencia Leonardi
We consider the problem of estimating the context tree of a stationary ergodic process with finite alphabet without imposing additional conditions on the process. As a starting poi…
Joint estimation of intersecting context tree models
Antonio Galves, Aurélien Garivier, Elisabeth Gassiat
We study a problem of model selection for data produced by two different context tree sources. Motivated by linguistic questions, we consider the case where the probabilistic conte…
Context Tree Selection: A Unifying View
Aurélien Garivier, Florencia Leonardi
The present paper investigates non-asymptotic properties of two popular procedures of context tree (or Variable Length Markov Chains) estimation: Rissanen's algorithm Context and t…
Exponential inequalities for empirical unbounded context trees
Antonio Galves, Florencia Leonardi
In this paper we obtain non-uniform exponential upper bounds for the rate of convergence of a version of the algorithm Context, when the underlying tree is not necessarily bounded.…
Some upper bounds for the rate of convergence of penalized likelihood context tree estimators
Florencia Leonardi
We find upper bounds for the probability of underestimation and overestimation errors in penalized likelihood context tree estimation. The bounds are explicit and applies to proces…
Testing statistical hypothesis on random trees and applications to the protein classification problem
Jorge R. Busch, Pablo A. Ferrari, Ana Georgina Flesia +3
Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statis…