A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

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.

Keywords
πps sampling designs; bootstrap; calibration; confidence intervals; finite populations; resampling; variance estimation

Journal
Bernoulli: Volume 26, Issue 2

• Published version