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Klopp - Sparse Network Estimation


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Auteur(s) :
Klopp Olga

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CEREMADE - UMR 7534
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Klopp - Sparse Network Estimation

Inhomogeneous random graph models encompass many network models such as stochastic block models and latent position models. We consider the problem of the statistical estimation of the matrix of connection probabilities based on the observations of the adjacency matrix of the network. We will also discuss the problem of graphon estimation when the probability matrix is sampled according to the graphon model. For these two problems, the minimax optimal rates of convergence in Frobenius norm are achieved by the least squares estimator which is known to be NP-hard. In this talk we will present two alternatives to the least squares: the hard thresholding estimator and the maximum likelihood estimator. We will provide new results for these two methods. We will also discuss the problem of link prediction commonly encountered in applications.

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    Date de réalisation : 1 Juillet 2019
    Lieu de réalisation : École Normale Supérieure, Paris.
    Durée du programme : 40 min
    Classification Dewey : Probabilités, Statistiques mathématiques, Mathématiques appliquées
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    Catégorie : Conférences, Cours magistraux, Séminaires
    Niveau : niveau Doctorat (LMD), Recherche
    Disciplines : Statistiques
    Collections : Summer School High Dimensional Probability and Algorithms HDPA 2019
    ficheLom : Voir la fiche LOM
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    Auteur(s) : Klopp Olga
    producteur : Lehec Joseph, Boyer Claire, Chafaï Djalil
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    Langue : Anglais
    Mots-clés : estimation, random graph, graphon
 

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