Institut de statistique

2015-6, Matei, Alina, Ranalli, Giovanna

2018-12-21, Grafström, Anton, Matei, Alina

2002, Dodge, Yadolah, Melfi, Giuseppe

2005-3-14, Chauvet, Guillaume, Tillé, Yves

2009-1-27, Matei, Alina, Skinner, Chris

2018-1-10, Tillé, Yves, Qualité, Lionel, Wilhelm, Matthieu

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.

2010-7, Matei, Alina, Tillé, Yves

2003-3-16, Chambers, Ray, Hulliger, Beat, Béguin, Cédric

2010-12-21, Haziza, David, Tillé, Yves

2014-6-27, Graf, Eric, Tillé, Yves

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.