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Hasler, Caren

Nom
Hasler, Caren
Affiliation principale
Fonction
Collaboratrice scientifique
Email
caren.hasler@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/542

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  • Publication
    Accès libre
  • Publication
    Accès libre
    New methods to handle nonresponse in surveys
    (2015)
    Hasler, Caren 
    ;
    Tillé, Yves 
    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.
  • Publication
    Accès libre
    Fast Balanced Sampling for Highly Stratified Population
    (2014-6)
    Hasler, Caren 
    ;
    Tillé, Yves 
    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.
  • Publication
    Accès libre
    Fast balanced sampling for highly stratified population
    Hasler, Caren 
    ;
    Tillé, Yves 
    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. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of strata. A new algorithm to select balanced samples in a stratified population is proposed. This new procedure is much faster than the existing one when the number of strata is large. Furthermore, this new procedure makes it possible to select samples for some large numbers of strata, which was impossible with the existing method. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. Finally, this algorithm turns out to be valuable for many applications as, for instance, for the handling of nonresponse