A formulation and an algorithm are presented to construct a truncated polynomial chaos representation of a vector-valued random output. This representation depends on a vector-valued random input with a known probability measure and a vector-valued random latent variable with an unknown probability measure. The construction of this PCE representati...
Abstract Statistical synthesis of data sets (meta-analysis, MA) has become a popular approach for providing scientific evidence to inform environmental and agricultural policy. As the number of published MAs is increasing exponentially, multiple MAs are now often available on a specific topic, delivering sometimes conflicting conclusions. To synthe...
Background Current clinical routines rely more and more on “omics” data such as flow cytometry data from host and microbiota. Cohorts variability in addition to patients’ heterogeneity and huge dimensions make it difficult to understand underlying structure of the data and decipher pathologies. Patients stratification and diagnostics from such comp...
High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in non-linear mixed-effects models where data are collected repeatedly on several individuals. In this work, variable selection is approached from a Bayesian perspective an...
The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-RER) is investigated under the assumption that the reference measure is a σ-finite measure, and not necessarily a probability measure. Under this assumption, which leads to a generalization of the ERM-RER problem allowing a larger degree of flexibility for incorpo...
We are motivated by the problem of identifying potentiallynonlinear regression relationships between high-dimensional outputs andhigh-dimensional inputs of heterogeneous data. This requires simultaneousregression, clustering, and model selection. In this framework, weconsider a case of mixture of experts models characterized by multipleGaussian exp...
Linear regression and classification models with repeated functional data are considered. For each statistical unit in the sample, a real-valued parameter is observed over time under different conditions. Two regression models based on fusion penalties are presented. The first one is a generalization of the variable fusion model based on the 1-near...
In the context of air quality control, our objective is to quantify the impact of uncertain inputs such as meteorological conditions and traffic parameters on pollutant dispersion maps. It is worth noting that the majority of sensitivity analysis methods are designed to deal with scalar or vector outputs and are ill suited to a map-valued output sp...
L’enquête de la Coordination des maraudes (ECM) vise à décrire les situations de rue observables à Paris pendant la journée. Cette enquête s’inscrit dans le cadre des travaux de redéfinition des territoires d’intervention des maraudes menés à la demande de la Direction régionale et interdépartementale de l’hébergement et du logement (DRIHL) et de l...
Stochastic optimization methods encounter new challenges in the realm of streaming, characterized by a continuous flow of large, high-dimensional data. While first-order methods, like stochastic gradient descent, are the natural choice, they often struggle with ill-conditioned problems. In contrast, second-order methods, such as Newton's methods, o...
