Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30897
Appears in Collections:Faculty of Health Sciences and Sport Journal Articles
Peer Review Status: Refereed
Title: A unified principled framework for resampling based on pseudo-populations: Asymptotic theory
Author(s): Conti, Pier Luigi
Marella, Daniela
Mecatti, Fulvia
Andreis, Federico
Contact Email: federico.andreis@stir.ac.uk
Keywords: πps sampling designs
bootstrap
calibration
confidence intervals
finite populations
resampling
variance estimation
Issue Date: 2020
Citation: Conti PL, Marella D, Mecatti F & Andreis F (2020) A unified principled framework for resampling based on pseudo-populations: Asymptotic theory. Bernoulli, 26 (2), pp. 1044-1069. https://doi.org/10.3150/19-bej1138
Abstract: In this paper, a class of resampling techniques for finite populations under πps sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in: (i) constructing a “pseudo-population” on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the “sampling design level”. Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.
DOI Link: 10.3150/19-bej1138
Rights: [BEJ1138_main.pdf] Publisher allows this work to be made available in this repository. Published in Bernoulli byBernoulli Society for Mathematical Statistics and Probability with the following policy: The authors retain the right to place the final version of their article (exactly as published in the journal) on their own homepage or in a public digital repository, provided there is a link to the official journal site: https://doi.org/10.3150/19-BEJ1138
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