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

## TL;DR

This paper introduces a unified resampling framework for finite populations under complex sampling, combining pseudo-population construction with resampling, supported by asymptotic theory and simulation results.

## Contribution

It proposes a novel two-step resampling method based on pseudo-populations, with new calibration techniques and theoretical validation for complex sampling designs.

## Key findings

- The framework is theoretically justified using large sample theory.
- New calibration methods improve pseudo-population accuracy.
- Simulation studies demonstrate the effectiveness of the approach.

## Abstract

In this paper, a class of resampling techniques for finite populations under complex sampling design is introduced. The basic idea on which it rests 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.