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

Exportar

Exportar

×

MLA

modern-language-association

APA

apa

Chicago

chicago-fullnote-bibliography

Harvard

harvard-cite-them-right

Vancouver

vancouver-author-date

---

RIS BibTex

Exportar

×

El documento no se puede exportar porque pertenece a otro portal.

Texto completo

lock_openTexto completo externo

DOI: 10.57645/20.8080.02.15 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Sort: Statistics and Operations Research Transactions

Resumen

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.

Fuente de los datos: Dialnet