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Convention Université de Neuchâtel/Office fédéral de la statistique
Titre du projet
Convention Université de Neuchâtel/Office fédéral de la statistique
Description
Convention cadre entre l'Université de Neuchâtel et l'Office fédéral de la statistique. Un programme de travail est établi chaque année. Les recherches portent sur le développement de méthodes statistiques dans le domaine de la statistique publique.
Chercheur principal
Statut
Completed
Date de début
1 Janvier 2001
Date de fin
1 Janvier 2016
Organisations
Identifiant interne
16871
identifiant
49 Résultats
Voici les éléments 1 - 10 sur 49
- PublicationMétadonnées seulementUne propriété intéressante de l'entropie de certains plans d'échantillonnage(2010-12-21)
- PublicationMétadonnées seulementVariance Estimation Using Linearization for Poverty and Social Exclusion Indicators(2014-6-27)
; We have used the generalized linearization technique based on the concept of influence function, as Osier has done (Osier 2009), to estimate the variance of complex statistics such as Laeken indicators. Simulations conducted using the R language show that the use of Gaussian kernel estimation to estimate an income density function results in a strongly biased variance estimate. We are proposing two other density estimation methods that significantly reduce the observed bias. One of the methods has already been outlined by Deville (2000). The results published in this article will help to significantly improve the quality of information on the precision of certain Laeken indicators that are disseminated and compared internationally. - PublicationMétadonnées seulementBibliographie sur la stratificationLa stratification d’une population en vue d’une enquête est en général basée sur une variable clé connue sur la population et bien corrélée avec les principales variables de l’enquête. Le problème de la définition de la stratification peut se décomposer en trois parties : 1. Recherche du nombre de classes ; 2. Calcul des limites de classes pour un nombre de classes donné ; 3. Allocation, c’est-à-dire détermination du nombre d’éléments à échantillonner dans chaque classe. La stratification multivariée base la définition des classes sur plusieurs variables. Le rapport est en cours de rédaction et comprend une bibliographie commentée d’environ 160 articles.
- PublicationMétadonnées seulementSGB R-package Simplicial Generalized Beta RegressionPackage SGB contains a generalization of the Dirichlet distribution, called the Simplicial Generalized Beta (SGB). It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation.
- PublicationMétadonnées seulementDiscretizing a compound distribution with application to categorical modelling(2017-2-17)
; Many probability distributions can be represented as compound distributions. Consider some parameter vector as random. The compound distribution is the expected distribution of the variable of interest given the random parameters. Our idea is to define a partition of the domain of definition of the random parameters, so that we can represent the expected density of the variable of interest as a finite mixture of conditional densities. We then model the mixture probabilities of the conditional densities using information on population categories, thus modifying the original overall model. We thus obtain specific models for sub-populations that stem from the overall model. The distribution of a sub-population of interest is thus completely specified in terms of mixing probabilities. All characteristics of interest can be derived from this distribution and the comparison between sub-populations easily proceeds from the comparison of the mixing probabilities. A real example based on EU-SILC data is given. Then the methodology is investigated through simulation. - PublicationMétadonnées seulementDesign-based Estimators Calibrated on Estimated Totals from Multiple Surveys(2017-8-1)
- PublicationMétadonnées seulementSGB R-package Simplicial Generalized Beta RegressionMain properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.
- PublicationMétadonnées seulementDiscretizing a compound distribution with application to categorical modelling. Part I: Methods(Neuchâtel Université de Neuchâtel Institut de Statistique, 2014)
; Many probability distributions can be represented as compound distributions. Consider some parameter vector as random. The compound distribution is the expected distribution of the variable of interest given the random parameters. Our idea is to de_ne a partition of the domain of de_nition of the random parameters, so that we can represent the expected density of the variable of interest as a _nite mixture of conditional densities. We then model the probabilities of the conditional densities using information on population categories, thus modifying the original overall model. Our examples uses the European Union Statistics on Income and Living Conditions (EU-SILC) data. For each country, we estimate a mixture model derived from the GB2 in which the probability weights are predicted with household categories. Comparisons across countries are processed using compositional data analysis tools. Our method also o_ers an indirect estimation of inequality and poverty indices. - PublicationMétadonnées seulementA generalized mixed model for skewed distributions applied to small area estimation(2019)Models with random (or mixed) effects are commonly used for panel data, in microarrays, small area estimation and many other applications.When the variable of interest is continuous, normality is commonly assumed, either in the original scale or after some transformation. However, the normal distribution is not always well suited for modeling data on certain variables, such as those found in Econometrics, which often show skewness even at the log scale. Finding the correct transformation to achieve normality is not straightforward since the true distribution is not known in practice. As an alternative, we propose to consider a much more flexible distribution called generalized beta of the second kind (GB2). The GB2 distribution contains four parameters with two of them controlling the shape of each tail, which makes it very flexible to accommodate different forms of skewness. Based on a multivariate extension of the GB2 distribution, we propose a new model with random effects designed for skewed response variables that extends the usual log-normal-nested error model. Under this new model, we find empirical best predictors of linear and nonlinear characteristics, including poverty indicators, in small areas. Simulation studies illustrate the good properties, in terms of bias and efficiency, of the estimators based on the proposed multivariate GB2 model. Results from an application to poverty mapping in Spanish provinces also indicate efficiency gains with respect to the conventional log-normalnested error model used for poverty mapping.
- PublicationMétadonnées seulement