Zobrazeno 1 - 10
of 273
pro vyhledávání: '"Robin Stephane"'
Autor:
Metodiev, Martin, Perrot-Dockès, Marie, Ouadah, Sarah, Fosdick, Bailey K., Robin, Stéphane, Latouche, Pierre, Raftery, Adrian E.
We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables can have spatial structure. This is motivated by the problem arising
Externí odkaz:
http://arxiv.org/abs/2411.04520
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
Publikováno v:
BMC Bioinformatics, Vol 10, Iss 1, p 84 (2009)
Abstract Background The use of current high-throughput genetic, genomic and post-genomic data leads to the simultaneous evaluation of a large number of statistical hypothesis and, at the same time, to the multiple-testing problem. As an alternative t
Externí odkaz:
https://doaj.org/article/90dd1219555640e1ac7a9a8e98d4952f
