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Institut de statistique
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Site web
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+41 (0) 32 718 13 80
Rue
Av. de Bellevaux 51
Code postal
2000
Ville
Neuchâtel
Pays
CH
Type d'institution
Academic Institute
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Voici les éléments 1 - 10 sur 554
- PublicationMétadonnées seulementDealing with nonignorable nonresponse in survey sampling: a latent variable modelling approach(2015-6)
; Ranalli, Giovanna - PublicationMétadonnées seulementOptimal sample coordination using controlled selection(2009-1-27)
; Skinner, Chris - PublicationMétadonnées seulementCoordination of spatially balanced samples(2018-12-21)
- PublicationMétadonnées seulementSampling Designs From Finite Populations With Spreading Control Parameters(2018-1-10)
; ; We present a new family of sampling designs in finite population based on the use of chain processes and of multivariate discrete distributions. In Bernoulli sampling, the number of non-selected units between two selected units has a geometric distribution, while, in simple random sampling, it has a negative hypergeometric distribution. We propose to replace these distributions by more general ones, which enables us to include a tuning parameter for the joint inclusion probabilities that have a relatively simple form. An effect of repulsion or attraction can then be added in the selection of the units in such a way that a large set of new designs are defined that include Bernoulli sampling, simple random sampling and systematic sampling. A set of simulations show the interest of the method. - PublicationMétadonnées seulementUne propriété intéressante de l'entropie de certains plans d'échantillonnage(2010-12-21)
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- PublicationMétadonnées seulementVariance Estimation Using Linearization for Poverty and Social Exclusion Indicators(2014-6-27)
; 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.