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    Sampling Designs From Finite Populations With Spreading Control Parameters
    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.
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    Quasi-Systematic Sampling From a Continuous Population
    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.
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    The Swiss Coordination System for Business Surveys
    The Swiss Coordination System for business surveys is operational since 2009. By september 2015 it has allowed to select 56 samples and subsamples for 20 different surveys. This system can coordinate a sequence of samples positively or negatively by maximizing or minimizing their expected overlap. The system also enables to define an order of priority on the past surveys with which the current sample is coordinated. It is compatible with a dynamic population where units can appear or disappear over time. It also allows for an unconstrained choice of inclusion probabilities for all units in the population. We will present the main principles of this system and discuss the main challenges we had to face for its implementation.
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  • Publication
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    Size constrained unequal probability sampling with a non-integer sum of inclusion probabilities
    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.
  • Publication
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    Tirages coordonnés d'échantillons poissoniens
    (Paris: Dunod, 2011) ; ; ;
    Tramblay, Marie-Eve
    ;
    Lavallée, Pierre
    ;
    El Haj Tirari, Mohammed
  • Publication
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    General framework for the rotation of units in repeated survey sampling
    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.
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  • Publication
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