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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
https://libra.unine.ch/handle/123456789/1547
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  • Publication
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  • Publication
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    Variance Estimation Using Linearization for Poverty and Social Exclusion Indicators
    (2014-6-27)
    Graf, Eric 
    ;
    Tillé, Yves 
    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)
    Graf, Monique 
    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.
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    Métadonnées seulement
  • Publication
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    SGB R-package Simplicial Generalized Beta Regression
    (Université de Neuchâtel Institut de statistique, 2018)
    Graf, Monique 
    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.
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    Discretizing a compound distribution with application to categorical modelling
    (2017-2-17)
    Graf, Monique 
    ;
    Nedyalkova, Desislava 
    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.
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    Design-based Estimators Calibrated on Estimated Totals from Multiple Surveys
    (2017-8-1)
    Guandalini, Alessio
    ;
    Tillé, Yves 
    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.
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    SGB R-package Simplicial Generalized Beta Regression
    (Institut de statistique Université de Neuchâtel, 2019-5-13)
    Graf, Monique 
    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.
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    Métadonnées seulement
    Imputation of income data with generalized calibration procedure and GB2 law: illustration with SILC data
    (2013-8-31)
    Graf, Eric 
    ;
    Tillé, Yves 
    In sample surveys of households and persons, questions about income are often sensitive and thus subject to a higher non-response rate. Nevertheless, the household or personal incomes are among the important variables in surveys of this type. The distribution of such collected incomes is not normal, neither log-normal. Hypotheses of classical regression models to explain the income (or their log) are not fulfilled. Imputations using such models modify the original and true distribution of the data. This is not suitable and may conduct the user to wrong interpretations of results computed from data imputed in this way. The generalized beta distribution of the second kind (GB2) is a four parameters distribution. Empirical studies have shown that it adapts very well to income data. The advantage of a parametric income distribution is that there exist explicit formulae for the inequality measures like the Laeken indicators as functions of the parameters. We present a parametric method of imputation, based on the fit of a GB2 law on the income distribution by the use of suitably adjusted weights obtained by generalized calibration. These weights can compensate for non ignorable non-response that affects the variable of interest. We validate our imputation system on data from the Swiss Survey on Income and Living Conditions (SILC).