Kernel Weighting for blending probability and non-probability survey samples

  1. María del Mar Rueda 1
  2. Beatriz Cobo 1
  3. Jorge Luis Rueda-Sánchez 1
  4. Ramon Ferri-García 1
  5. Luis Castro-Martín 1
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

Journal:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Year of publication: 2024

Volume: 48

Issue: 1

Pages: 93-124

Type: Article

DOI: 10.57645/20.8080.02.15 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Sort: Statistics and Operations Research Transactions

Abstract

In this paper we review some methods proposed in the literature for combining a nonprobability and a probability sample with the purpose of obtaining an estimator with a smaller bias and standard error than the estimators that can be obtained using only the probability sample. We propose a new methodology based on the kernel weighting method. We discuss the properties of the new estimator when there is only selection bias and when there are both coverage and selection biases. We perform an extensive simulation study to better understand the behaviour of the proposed estimator.

Data source: Dialnet