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Hasler, Caren
Nom
Hasler, Caren
Affiliation principale
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Collaboratrice scientifique
Email
caren.hasler@unine.ch
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Voici les éléments 1 - 10 sur 17
- PublicationAccès libreNonparametric imputation method for nonresponse in surveys(2020)
; Craiu, Radu V. - PublicationMétadonnées seulementBalanced k-Nearest Neighbor Imputation(2016-5-22)
; In order to overcome the problem of item nonresponse, random imputation methods are often used because they tend to preserve the distribution of the imputed variable. Among the random i.mputation methods, the random hot-deck has the interesting property of imputing observed values. A new random hot-deck imputation method is proposed. The key innovation of this method is that the selection of donors is viewed as a sampling problem and uses calibration and balanced sampling. This approach makes it possible to select donors such that if the auxiliary variables were imputed, their estimated totals would not change. As a consequence, very accurate and stable totals estimations can be obtained. Moreover, donors are selected in neighborhoods of recipients. In this way, the missing value of a recipient is replaced with an observed value of a similar unit. This second approach can greatly improve the quality of estimations. Finally, these two approaches imply underlying models and the method is resistent to model misspecification. - PublicationMétadonnées seulement
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- PublicationMétadonnées seulementFormula for revenue equalization with progressive redistribution rates(2015-3-1)
; ; Revenue equalization consists of reducing disparity in taxing power between cantons or municipalities (hereafter administrative divisions) within cantons, transferring fiscal revenue from strong taxing power administrative divisions to weak taxing power administrative divisions. A method for revenue equalization leads a constant redistribution if the transferred amounts are computed independently from the taxing power of administrative divisions. By contrast, a method for revenue equalization leads a progressive redistribution if it takes the taxing power into account in the transferred amounts computation. Hence, the amounts transferred are negligible for administrative divisions with a taxing power close to the mean taxing power and these amounts increase as the taxing power of administrative divisions moves away from the mean taxing power. A formula for revenue equalization is proposed. This formula induces a progressive redistribution and makes it possible for the user to control the strength of the progressiveness. A method based on the Gini index is proposed in order to optimally tune the redistribution rates under some constraints. - PublicationAccès libreNew methods to handle nonresponse in surveys(2015)
; Ce document porte sur la nonréponse dans les enquêtes par échantillonnage. Principalement, des méthodes de traitement de la nonréponse dans des enquêtes complexes sont proposées. Le premier chapitre de ce document introduit des concepts relatifs à l'échantillonnage et à la nonréponse. Le second chapitre propose un algorithme d'échantillonnage équilibré pour des populations hautement stratifiées. Le troisième chapitre de ce document propose une méthode d'imputation par donneur dont la sélection se fait par échantillonnage équilibré combiné à une approche nonparamétrique. Cette méthode nécessite l'utilisation de l'algorithme faisant l'objet du second chapitre. Le chapitre qui suit présente une méthode d'imputation nonparamétrique basée sur les modèles de régression additifs. Finalement, le cinquième chapitre propose trois procédures de repondération pour le traitement de la nonréponse non-ignorable applicable lorsque les valeurs prises par la variable d'intérêt proviennent d'une densité mélange., This document focuses on nonresponse in sample surveys. Mainly, methods to handle nonresponse in complex surveys are proposed. The first chapter of this document introduces concepts and notation of survey sampling and nonresponse. The second chapter proposes an algorithm for stratified balanced sampling for populations with large numbers of strata. The third chapter of this document presents a hot-deck imputation method which combines balanced sampling and a nonparametric approach. This method uses the algorithm presented in the second chapter. The next chapter presents a nonparametric method of imputation for item nonresponse in surveys based on additive regression models. Finally, the fifth chapter proposes three reweighting procedures for handling nonignorable nonresponse in surveys providing that the values of the variable of interest are obtained from a mixture distribution. - PublicationAccès libre
- PublicationAccès libreFast Balanced Sampling for Highly Stratified Population(2014-6)
; Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. Chauvet (2009) proposed a procedure to select balanced samples in a stratified population. Unfortunately, Chauvet's procedure can be slow when the number of strata is very large. In this paper, we propose a new algorithm to select balanced samples in a stratified population. This new procedure is at the same time faster and more accurate than Chauvet's. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. This algorithm turns out to be valuable for many applications. For instance, it can improve the quality of the estimates produced by multistage surveys for which only one or two primary sampling units are selected in each stratum. Moreover, this algorithm may be used to treat nonresponse. - PublicationMétadonnées seulement