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    Métadonnées seulement
  • Publication
    Métadonnées seulement
    Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators
    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.
  • Publication
    Métadonnées seulement
    Bibliographie sur la stratification
    (Université de Neuchâtel Institut de statistique, 2014)
    La 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.
  • Publication
    Accès libre
    The simplicial generalized beta distribution. R-package and applications
    (2019-6-8)
    A generalization of the Dirichlet and the scaled Dirichlet distributions is given by the simplicial generalized Beta, SGB (Graf, 2017). In the Dirichlet and the scaled Dirichlet distributions, the shape parameters are modeled with auxiliary variables (Maier, 2015, R-package DirichletReg) and Monti et al. (2011), respectively. On the other hand, in the ordinary logistic normal regression, it is the scale composition that is made dependent on auxiliary variables. The modeling of scales seems easier to interpret than the modeling of shapes. Thus in the SGB regression: - The scale compositions are modeled in the same way as for the logistic normal regression, i.e. each auxiliary variable generates D parameters, where D is the number of parts. - The D Dirichlet shape parameters, one for each part in the compositions, are estimated as well. - An additional overall shape parameter is introduced in the SGB that proves to have important properties in relation with non essential zeros. - Use of survey weights is an option. - Imputation of missing parts is possible. An application to the United Kingdom Time Use Survey (Gershuny and Sullivan, 2017) shows the power of the method. The R-package SGB (Graf, 2019) makes the method accessible to users. See the package vignette for more information and examples.
  • Publication
    Métadonnées seulement
  • Publication
    Métadonnées seulement
    SGB R-package Simplicial Generalized Beta Regression
    (Université de Neuchâtel Institut de statistique, 2018)
    Package 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.
  • Publication
    Métadonnées seulement
    Discretizing a compound distribution with application to categorical modelling
    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.
  • Publication
    Métadonnées seulement
    Design-based Estimators Calibrated on Estimated Totals from Multiple Surveys
    (2017-8-1)
    Guandalini, Alessio
    ;
    The use of auxiliary variables to improve the efficiency of estimators is a well-known strategy in survey sampling. Typically, the auxiliary variables used are the totals of appropriate measurement that are exactly known from registers or administrative sources. Increasingly, however, these totals are estimated from surveys and are then used to calibrate estimators and improve their efficiency. We consider different types of survey structures and develop design-based estimators that are calibrated on known as well as estimated totals of auxiliary variables. The optimality properties of these estimators are studied. These estimators can be viewed as extensions of the Montanari generalised regression estimator adapted to the more complex situations. The paper studies interesting special cases to develop insights and guidelines to properly manage the survey-estimated auxiliary totals.
  • Publication
    Métadonnées seulement
    SGB R-package Simplicial Generalized Beta Regression
    (Institut de statistique Université de Neuchâtel, 2019-5-13)
    Main 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.