Search results for "DISTRIBUTIONS"

Interactions, spillovers de connaissance et croissance des villes européennes. Faut-il préférer la géographie, le climat institutionnel ou les réseau…

2013

Knowledge spillovers within urban economies are also sources of spillovers between cities. We examine how knowledge spillovers influenced the economic growth of 82 European metropolises over the 1990-2005 period. We model knowledge spillovers between cities on the basis of five specific interaction patterns based on geography, networks of multinational firms in advanced services, institutional climate and two combinations of these factors. Spatial models are estimated to detail the effects of growth factors in terms of spillovers and externalities. We show that spillovers are local rather than global and that interactions among cities accelerate the convergence process based on gross value …

Le paradoxe du seuil de pauvreté endogène dans les indices de pauvreté

2007

Proximité et formation des villes : le rôle des externalités d’information

1998

International audience

Mesure de la section efficace de l'électroproduction de photons à JLAB dans le but d'effectuer une Séparation Rosenbluth de la contribution DVCS

2014

The study of the inner structure of hadrons allows us to understand the nature of the interactions between partons, quarks and gluons, described by Quantum Chromodynamics. The elastic scattering reactions, which have been studied in order to measure the nucleon form factors, are included in this frame. The inelastic scattering reactions are also included in this frame, they allow us to obtain information about the nucleon structure thanks to the development of the parton distribution functions (PDFs). While through elastic scattering we can obtain information about the charge distribution of the nucleon, and hence, about the spatial distribution of the partons, through inelastic scattering …

Learning with the kernel signal to noise ratio

2012

This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in nonlinear regression examples, dependence estimation and causal inference, nonlinear channel equalization, and nonlinear feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extract…

A characterization of the distribution of a weighted sum of gamma variables through multiple hypergeometric functions

2008

Applying the theory on multiple hypergeometric functions, the distribution of a weighted convolution of Gamma variables is characterized through explicit forms for the probability density function, the distribution function and the moments about the origin. The main results unify some previous contributions in the literature on nite convolution of Gamma distributions. We deal with computational aspects that arise from the representations in terms of multiple hypergeometric functions, introducing a new integral representation for the fourth Lauricella function F (n) D and its con uent form (n) 2 , suitable for numerical integration; some graphics of the probability density function and distr…

Random effects elliptically distributed in unbalanced linear models

2008

In linear mixed effects models, random effects are used for modelling the variance-covariance structure of the response variable. These models are based on the assumption that the random effects are normally distributed, but in literature alternative random effect distributions have been proposed and the consequences of misspecification are investigated. These studies consider only balanced designs. Aim of this paper is to study an unbalanced linear mixed model with random effects elliptically distributed.

Differential models of twin correlations in skew for body-mass index (BMI)

2018

Background Body Mass Index (BMI), like most human phenotypes, is substantially heritable. However, BMI is not normally distributed; the skew appears to be structural, and increases as a function of age. Moreover, twin correlations for BMI commonly violate the assumptions of the most common variety of the classical twin model, with the MZ twin correlation greater than twice the DZ correlation. This study aimed to decompose twin correlations for BMI using more general skew-t distributions. Methods Same sex MZ and DZ twin pairs (N = 7,086) from the community-based Washington State Twin Registry were included. We used latent profile analysis (LPA) to decompose twin correlations for BMI into mul…

Epigenetic outlier profiles in depression: A genome-wide DNA methylation analysis of monozygotic twins

2018

Recent discoveries highlight the importance of stochastic epigenetic changes, as indexed by epigenetic outlier DNA methylation signatures, as a valuable tool to understand aberrant cell function and subsequent human pathology. There is evidence of such changes in different complex disorders as diverse as cancer, obesity and, to a lesser extent, depression. The current study was aimed at identifying outlying DNA methylation signatures of depressive psychopathology. Here, genome-wide DNA methylation levels were measured (by means of Illumina Infinium HumanMethylation450 Beadchip) in peripheral blood of thirty-four monozygotic twins informative for depressive psychopathology (lifetime DSM-IV d…

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

2014

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-…