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Qualité, Lionel
Nom
Qualité, Lionel
Affiliation principale
Fonction
maître assistant
Email
lionel.qualite@unine.ch
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Voici les éléments 1 - 10 sur 12
- 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 seulementQuasi-Systematic Sampling From a Continuous Population(2017)
; ; A specific family of point processes are introduced that allow to select samples for the purpose of estimating the mean or the integral of a function of a real variable. These processes, called quasi-systematic processes, depend on a tuning parameter $r>0$ that permits to control the likeliness of jointly selecting neighbor units in a same sample. When $r$ is large, units that are close tend to not be selected together and samples are well spread. When $r$ tends to infinity, the sampling design is close to systematic sampling. For all $r > 0$, the first and second-order unit inclusion densities are positive, allowing for unbiased estimators of variance. Algorithms to generate these sampling processes for any positive real value of $r$ are presented. When $r$ is large, the estimator of variance is unstable. It follows that $r$ must be chosen by the practitioner as a trade-off between an accurate estimation of the target parameter and an accurate estimation of the variance of the parameter estimator. The method's advantages are illustrated with a set of simulations. - 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. - PublicationMétadonnées seulementSize constrained unequal probability sampling with a non-integer sum of inclusion probabilities(2012)
; ; More than 50 methods have been developed to draw unequal probability samples with fixed sample size. All these methods require the sum of the inclusion probabilities to be an integer number. There are cases, however, where the sum of desired inclusion probabilities is not an integer. Then, classical algorithms for drawing samples cannot be directly applied. We present two methods to overcome the problem of sample selection with unequal inclusion probabilities when their sum is not an integer and the sample size cannot be fixed. The first one consists in splitting the inclusion probability vector. The second method is based on extending the population with a phantom unit. For both methods the sample size is almost fixed, and equal to the integer part of the sum of the inclusion probabilities or this integer plus one. - PublicationMétadonnées seulementGeneral framework for the rotation of units in repeated survey sampling(2009-3-23)
; ; Coordination of probabilistic samples is a challenging theoretical problem faced by statistical institutes. One of their aims is to obtain good estimates for each wave while spreading the response burden across the entire population. There is a collection of existing solutions that try to attend to these needs. These solutions, which were developed independently, are integrated in a general framework and their corresponding longitudinal designs are computed. The properties of these longitudinal designs are discussed. It is also noted that there is an antagonism between a good rotation and control over the cross-sectional sampling design. A compromise needs to be reached between the quality of the sample coordination, which appears to be optimal for a systematic longitudinal sampling design, and the freedom of choice of the cross-sectional design. In order to reach such a compromise, an algorithm that uses a new method of longitudinal sampling is proposed. - PublicationMétadonnées seulement
- PublicationMétadonnées seulementA comparison of conditional Poisson sampling versus unequal probability sampling with replacement(2008-4-5)The variance of the Horvitz–Thompson estimator for a fixed size ConditionalPoissonsampling scheme without replacement and with unequal inclusion probabilities is compared to the variance of the Hansen–Hurwitz estimator for asampling scheme with replacement. We show, using a theorem by Gabler, that the sampling design without replacement is more efficient than the sampling design with replacement.
- PublicationMétadonnées seulementVariance estimation of changes in repeated surveys and its application to the Swiss survey of value added(2008-3-23)
; We propose a method for estimating the variance of estimators of changes over time, a method that takes account of all the components of these estimators: the sampling design, treatment of non-response, treatment of large companies, correlation of non-response from one wave to another, the effect of using a panel, robustification, and calibration using a ratio estimator. This method, which serves to determine the confidence intervals of changes over time, is then applied to the Swiss survey of value added. - PublicationMétadonnées seulementSystematic sampling is a minimal support design(2007-3-23)
; ; In order to select a sample in a finite population of N units with given inclusion probabilities, it is possible to define asamplingdesign on at most N samples that have a positive probability of being selected. Designs defined on minimal sets of samples are called minimum supportdesigns. It is shown that, for any vector of inclusion probabilities, systematicsampling always provides a minimum supportdesign. This property makes it possible to extensively compute the samplingdesign and the joint inclusion probabilities. Random systematicsampling can be viewed as the random choice of a minimum supportdesign. However, even if the population is randomly sorted, a simple example shows that some joint inclusion probabilities can be equal to zero. Another way of randomly selecting a minimum supportdesign is proposed, in such a way that all the samples have a positive probability of being selected, and all the joint inclusion probabilities are positive. - PublicationAccès libreVariance estimation of changes in repeated surveys and its application to the Swiss survey of value addedWe propose a method for estimating the variance of estimators of changes over time, a method that takes account of all the components of these estimators: the sampling design, treatment of non-response, treatment of large companies, correlation of non- response from one wave to another, the effect of using a panel, robustification, and calibration using a ratio estimator. This method, which serves to determine the confidence intervals of changes over time, is then applied to the Swiss survey of value added.