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Institut de statistique
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+41 (0) 32 718 13 80
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Av. de Bellevaux 51
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2000
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Neuchâtel
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CH
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Academic Institute
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555 Résultats
Voici les éléments 1 - 10 sur 555
- PublicationMétadonnées seulement
- PublicationMétadonnées seulementBootstrap Methods for Two-Phase Sampling With Poisson Design at the Second PhaseIn order to provide an unbiased estimator of the variance, the most frequently used sampling design in existing bootstrap methods is simple random sampling with replacement. Nevertheless when these methods do not take the sampling design into account, they provide biased variance estimators. Resampled units usually need to be rescaled or weighted to correct this bias. Another set of methods consists of constructing artificial populations and to resampling from them. These methods are very often time-consuming and have rounding problems. Furthermore, when the sampling design is of several phases, implementation of these bootstrap methods becomes very difficult. We present new sampling methods for two-phase sampling with Poisson design at the second-phase. In this paper, only the cases where the second phase design is Poisson are considered. The reason for this restriction is the connection between this type of two-phase design and lots of missing data problems where the non response is not ignorable. These methods consist of resampling only a subsample of the units in the second phase. This subsample is selected randomly in such a way that it directly reproduces the appropriate variance, without having to rescale or create artificial population. The main advantage of the method is its simplicity, especially for after treatments, such as calibration or imputation for nonresponse. These techniques can be directly applied to bootstrap samples. That is why the proposed method could be particularly worthwhile in real applications.
- PublicationMétadonnées seulementAdjusting for nonignorable nonresponse using a latent variable modeling approach(2011-8)
; Ranalli, Giovanna - PublicationAccès libreA new resampling method for sampling designs without replacement: the doubled half bootstrapA new and very fast method of bootstrap for sampling without replacement from a finite population is proposed. This method can be used to estimate the variance in sampling with unequal inclusion probabilities and does not require artificial populations or utilization of bootstrap weights. The bootstrap samples are directly selected from the original sample. The bootstrap procedure contains two steps: in the first step, units are selected once with Poisson sampling using the same inclusion probabilities as the original design. In the second step, amongst the non-selected units, half of the units are randomly selected twice. This procedure enables us to efficiently estimate the variance. A set of simulations show the advantages of this new resampling method.
- PublicationMétadonnées seulement
- PublicationMétadonnées seulement
- PublicationAccès libreTen years of balanced sampling with the cube method: An appraisalThis paper presents a review and assessment of the use of balanced sampling by means of the cube method. After defining the notion of balanced sample and balanced sampling, a short history of the concept of balancing is presented. The theory of the cube method is briefly presented. Emphasis is placed on the practical problems posed by balanced sampling: the interest of the method with respect to other sampling methods and calibration, the field of application, the accuracy of balancing, the choice of auxiliary variables and ways to implement the method.
- PublicationMétadonnées seulement
- PublicationMétadonnées seulementSondage dans des registres de population et de ménages en Suisse : coordination d’échantillons, pondération et imputation(2014-11-24)
; L’Office Fédéral de la Statistique harmonise ses enquêtes par échantillonnage auprès des personnes et des ménages en Suisse. Dans cet article, nous présentons un aperçu des méthodes actuellement utilisées. Les échantillons sont sélectionnés de manière coordonnée afin de répartir au mieux la charge d’enquête sur les ménages et les personnes. Le calcul des pondérations, dont on présente les principales étapes, est adapté aux différents besoins et aux différentes situations rencontrées. L’Office se base sur les recommandations internationales, dont il participe à l’élaboration, pour le traitement des données d’enquête et les imputations. La précision des estimateurs est systématiquement évaluée en tenant compte des traitements réalisés.