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May 2020 A unified principled framework for resampling based on pseudo-populations: Asymptotic theory
Pier Luigi Conti, Daniela Marella, Fulvia Mecatti, Federico Andreis
Bernoulli 26(2): 1044-1069 (May 2020). DOI: 10.3150/19-BEJ1138


In this paper, a class of resampling techniques for finite populations under $\pi $ps 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.


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Pier Luigi Conti. Daniela Marella. Fulvia Mecatti. Federico Andreis. "A unified principled framework for resampling based on pseudo-populations: Asymptotic theory." Bernoulli 26 (2) 1044 - 1069, May 2020.


Received: 1 December 2017; Revised: 1 May 2019; Published: May 2020
First available in Project Euclid: 31 January 2020

zbMATH: 07166556
MathSciNet: MR4058360
Digital Object Identifier: 10.3150/19-BEJ1138

Keywords: $\pi $ps sampling designs , bootstrap , Calibration , confidence intervals , finite populations , Resampling , variance estimation

Rights: Copyright © 2020 Bernoulli Society for Mathematical Statistics and Probability


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Vol.26 • No. 2 • May 2020
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