Pierre Barbillon

Bio: Pierre Barbillon is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Stochastic block model & Computer experiment. The author has an hindex of 12, co-authored 65 publications receiving 485 citations. Previous affiliations of Pierre Barbillon include University of Paris-Sud & Institut national de la recherche agronomique.

Patterns of plant and animal protein intake are strongly associated with cardiovascular mortality: the Adventist Health Study-2 cohort.

Abstract: Background: Current evidence suggests that plant and animal proteins are intimately associated with specific large nutrient clusters that may explain part of their complex relation with cardiovascular health. We aimed at evaluating the association between specific patterns of protein intake with cardiovascular mortality. Methods: We selected 81 337 men and women from the Adventist Health Study-2. Diet was assessed between 2002 and 2007, by using a validated food frequency questionnaire. Dietary patterns based on the participants' protein consumption were derived by factor analysis. Cox regression analysis was used to estimate multivariate-adjusted hazard ratios (HRs) adjusted for sociodemographic and lifestyle factors and dietary components. Results: There were 2276 cardiovascular deaths during a mean follow-up time of 9.4 years. The HRs for cardiovascular mortality were 1.61 [98.75% confidence interval (CI), 1.12 2.32; P-trend < 0.001] for the 'Meat' protein factor and 0.60 (98.75% CI, 0.42 0.86; P-trend < 0.001) for the 'Nuts & Seeds' protein factor (highest vs lowest quintile of factor scores). No significant associations were found for the 'Grains', 'Processed Foods' and 'Legumes, Fruits & Vegetables' protein factors. Additional adjustments for the participants' vegetarian dietary pattern and nutrients related to cardiovascular disease outcomes did not change the results. Conclusions: Associations between the 'Meat' and 'Nuts & Seeds' protein factors and cardiovascular outcomes were strong and could not be ascribed to other associated nutrients considered to be important for cardiovascular health. Healthy diets can be advocated based on protein sources, preferring low contributions of protein from meat and higher intakes of plant protein from nuts and seeds.

Stochastic block models for multiplex networks: an application to a multilevel network of researchers

Abstract: Modelling relationships between individuals is a classical question in social sci- ences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relation- ship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation–maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possi- ble links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.

Analyzing Stochastic Computer Models: A Review with Opportunities

Abstract: In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.

Variational Inference for Stochastic Block Models From Sampled Data

Abstract: This article deals with nonobserved dyads during the sampling of a network and consecutive issues in the inference of the stochastic block model (SBM). We review sampling designs and recover missin...

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.