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Tillé, Yves

Nom
Tillé, Yves
Affiliation principale
Site web
Fonction
Professeur ordinaire
Email
yves.tille@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/541
_
0000-0003-0904-5523

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  • Publication
    Accès libre
    Linearization and Variance Estimation of the Bonferroni Inequality Index
    (Neuchâtel Institut de Statistique Faculté des sciences, 2021)
    Dong, Ziqing 
    ;
    Tillé, Yves 
    ;
    Giorgi, Giovanni M.
    ;
    Guandalini, Alessio
    The study of income inequality is important for predicting the wealth of a country. There is an increasing number of publications where the authors call for the use of several indices simultaneously to better account for the wealth distribution. Due to the fact that income data are usually collected through sample surveys, the sampling properties of income inequality measures should not be overlooked. The most widely used inequality measure is the Gini index, and its inferential aspects have been deeply investigated. An alternative inequality index could be the Bonferroni inequality index, although less attention on its inference has been paid in the literature. The aim of this paper is to address the inference of the Bonferroni index in a finite population framework. The Bonferroni index is linearized by differentiation with respect to the sample indicators which allows for conducting a valid inference. Furthermore, the linearized variables are used to evaluate the effects of the different observations on the Bonferroni and Gini indices. The result demonstrates once for all that the former is more sensitive to the lowest incomes in the distribution than the latter.
  • Publication
    Métadonnées seulement
    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.