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Tillé, Yves

Nom
Tillé, Yves
Affiliation principale
Site web
Fonction
Professeur ordinaire
Email
yves.tille@unine.ch
Identifiants
https://libra.unine.ch/handle/123456789/541
_
0000-0003-0904-5523

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  • Publication
    Métadonnées seulement
    Incorporating spatial and operational constraints in the sampling designs for forest inventories
    (2015-6-15)
    Vallée, Audrey-Anne 
    ;
    Ferland-Raymond, Bastien
    ;
    Rivest, Louis-Paul
    ;
    Tillé, Yves 
    In the province of Quebec, Canada, the forest is examined through regular inventories. Requirements for the spreading and the type of trees and for the cost are difficult to manage. We show that modern and advanced sampling techniques can be used to improve the planning of the forest inventories, even if there are many requirements. Our design includes balanced sampling, highly stratified balanced sampling and sample spreading through a two stage sample. The impact of these techniques on the satisfaction of the requirements and on the precision of survey estimates is investigated using field data from a Quebec inventory.
  • Publication
    Accès libre
    Fast Balanced Sampling for Highly Stratified Population
    (2014-6)
    Hasler, Caren 
    ;
    Tillé, Yves 
    Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. Chauvet (2009) proposed a procedure to select balanced samples in a stratified population. Unfortunately, Chauvet's procedure can be slow when the number of strata is very large. In this paper, we propose a new algorithm to select balanced samples in a stratified population. This new procedure is at the same time faster and more accurate than Chauvet's. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. This algorithm turns out to be valuable for many applications. For instance, it can improve the quality of the estimates produced by multistage surveys for which only one or two primary sampling units are selected in each stratum. Moreover, this algorithm may be used to treat nonresponse.
  • Publication
    Accès libre
    Fast balanced sampling for highly stratified population
    Hasler, Caren 
    ;
    Tillé, Yves 
    Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced. A procedure to select balanced samples in a stratified population has previously been proposed. Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of strata. A new algorithm to select balanced samples in a stratified population is proposed. This new procedure is much faster than the existing one when the number of strata is large. Furthermore, this new procedure makes it possible to select samples for some large numbers of strata, which was impossible with the existing method. Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum. Finally, this algorithm turns out to be valuable for many applications as, for instance, for the handling of nonresponse
  • Publication
    Accès libre
    Variance approximation under balanced sampling
    Deville, Jean-Claude
    ;
    Tillé, Yves 
    A balanced sampling design has the interesting property that Horvitz–Thompson estimators of totals for a set of balancing variables are equal to the totals we want to estimate, therefore the variance of Horvitz–Thompson estimators of variables of interest are reduced in function of their correlations with the balancing variables. Since it is hard to derive an analytic expression for the joint inclusion probabilities, we derive a general approximation of variance based on a residual technique. This approximation is useful even in the particular case of unequal probability sampling with fixed sample size. Finally, a set of numerical studies with an original methodology allows to validate this approximation.