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A new replicate variance estimator for unequal probability sampling without replacement

Escobar, Emilio L. and Berger, Yves G. (2013) A new replicate variance estimator for unequal probability sampling without replacement Canadian Journal of Statistics, 41, (3), pp. 508-524. (doi:10.1002/cjs.11187).

Record type: Article


We propose a new replicate variance estimator suitable for differentiable functions of estimated totals. The proposed variance estimator is defined for any unequal-probability without-replacement sampling design, it naturally includes finite population corrections and it allows two-stage sampling. We show its design-consistency and its close relationship with linearization variance estimators. When estimating a total, the proposed estimator reduces to the Horvitz-Thompson variance estimator. Monte-Carlo simulations suggest that the proposed variance estimator is more stable than its replicate competitors.

Nous proposons un nouvel estimateur de variance ré-échantillonnés adapté à des fonctions dérivables de totaux estimés. L'estimateur de variance proposé est défini pour tous les plans d’échantillonnage à probabilités inégales sans remise. Il comprend naturellement les corrections de population finie et il peut s'appliquer à l’échantillonnage à deux degrés. Nous montrons sa convergence asymptotique sous le plan d’échantillonnage et sa relation avec les estimateurs linéarisés de variance. Lors de l'estimation d'un total, l'estimateur proposé se réduit à l'estimateur de variance de Horvitz-Thompson. Des simulations suggèrent que l'estimateur de variance proposée est plus stable que ses concurrents.

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e-pub ahead of print date: 19 July 2013
Published date: September 2013
Related URLs:
Organisations: Statistical Sciences Research Institute


Local EPrints ID: 350428
ISSN: 0319-5724
PURE UUID: c7036023-3251-45d1-b8f0-8b4bf8962864

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Date deposited: 04 Apr 2013 10:47
Last modified: 18 Jul 2017 04:34

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Author: Emilio L. Escobar
Author: Yves G. Berger

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