Publication :
Sampling a two dimensional matrix

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Date
2020-04-04
Auteurs
Ewane Ebouele, Sergio
Direction de publication
Direction de recherche
Titre de la revue
ISSN de la revue
Titre du volume
Éditeur
Elsevier
Projets de recherche
Structures organisationnelles
Numéro de revue
Résumé
A new sampling design for populations whose units can be arranged as an matrix is proposed. The sample must satisfy some constraints: row and column sample sizes are set in advance. The proposed sampling method gives the same selection probability to all the sample matrices that satisfy the constraints. Three algorithms to select a sample uniformly in the feasible set are presented: an exact algorithm based on the multivariate hypergeometric distribution, an MCMC algorithm, and the cube method. Their performances are evaluated using Monte Carlo simulations. The designs for sampling elements in a given row or a given column are investigated and the single inclusion and joint selection probabilities under the proposed design are evaluated. Several variance estimators are proposed for the Horvitz–Thompson estimator of the population mean of the survey variable and their performances are compared in a Monte Carlo study. A numerical example dealing with a creel survey of fishermen found at 9 sites over 36 days is presented.
Description
Revue
Computational statistics and data analysis, Vol. 149 (2020)
DOI
10.1016/j.csda.2020.106971
URL vers la version publiée
Mots-clés
Balanced sampling , Creel survey , Cube method , Multivariate hypergeometric distribution , Monte Carlo Markov Chain
Citation
Type de document
article de recherche