Zobrazeno 1 - 10
of 442
pro vyhledávání: '"Robin, Stéphane"'
We consider the robust estimation of the parameters of multivariate Gaussian linear regression models. To this aim we consider robust version of the usual (Mahalanobis) least-square criterion, with or without Ridge regularization. We introduce two me
Externí odkaz:
http://arxiv.org/abs/2404.19496
Autor:
Stoehr, Julien, Robin, Stephane S.
Inferring parameters of a latent variable model can be a daunting task when the conditional distribution of the latent variables given the observed ones is intractable. Variational approaches prove to be computationally efficient but, possibly, lack
Externí odkaz:
http://arxiv.org/abs/2402.14390
We consider a broad class of random bipartite networks, the distribution of which is invariant under permutation within each type of nodes. We are interested in $U$-statistics defined on the adjacency matrix of such a network, for which we define a n
Externí odkaz:
http://arxiv.org/abs/2308.14518
Grouping observations into homogeneous groups is a recurrent task in statistical data analysis. We consider Gaussian Mixture Models, which are the most famous parametric model-based clustering method. We propose a new robust approach for model-based
Externí odkaz:
http://arxiv.org/abs/2211.08131
Motivation: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as sets of p-values resulting from
Externí odkaz:
http://arxiv.org/abs/2104.14601
Bipartite networks are a natural representation of the interactions between entities from two different types. The organization (or topology) of such networks gives insight to understand the systems they describe as a whole. Here, we rely on motifs w
Externí odkaz:
http://arxiv.org/abs/2101.11381
Autor:
Robin, Stéphane, Scrucca, Luca
The entropy is a measure of uncertainty that plays a central role in information theory. When the distribution of the data is unknown, an estimate of the entropy needs be obtained from the data sample itself. We propose a semi-parametric estimate, ba
Externí odkaz:
http://arxiv.org/abs/2010.04058
Network inference aims at unraveling the dependency structure relating jointly observed variables. Graphical models provide a general framework to distinguish between marginal and conditional dependency. Unobserved variables (missing actors) may indu
Externí odkaz:
http://arxiv.org/abs/2007.14299
Autor:
Donnet, Sophie, Robin, Stéphane
This work is motivated by the analysis of ecological interaction networks. Poisson stochastic blockmodels are widely used in this field to decipher the structure that underlies a weighted network, while accounting for covariate effects. Efficient alg
Externí odkaz:
http://arxiv.org/abs/1907.09771
The behavior of ecological systems mainly relies on the interactions between the species it involves. We consider the problem of inferring the species interaction network from abundance data. To be relevant, any network inference methodology needs to
Externí odkaz:
http://arxiv.org/abs/1905.02452