Geographically weighted regression model-assisted estimation in survey sampling

Chao Liu, Chuanhua Wei and Yunan Su

Journal of Nonparametric Statistics, 2018, vol. 30, issue 4, 906-925

Abstract: A geographically weighted regression model-assisted method is proposed to estimate the finite population totals using survey data with the aid of spatial and other auxiliary information. The local linear method is used to the estimation of geographically weighted regression model. Our proposed GWR-assisted (geographically weighted regression model-assisted) estimators are more efficient than the well-known Horvitz–Thompson estimators. These estimators are calibrated and asymptotically design-unbiased. Some theoretical results have been established for GWR-assisted estimators. Simulation experiments show that the GWR-assisted estimators are more efficient than the LM-assisted (linear regression model-assisted) estimators and NP-assisted (nonparametric regression model-assisted) estimators. Finally, the Boston housing data are used in the simulation study to demonstrate the importance of location information in spatial modelling.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/10485252.2018.1499907 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:gnstxx:v:30:y:2018:i:4:p:906-925

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GNST20

DOI: 10.1080/10485252.2018.1499907

Access Statistics for this article

Journal of Nonparametric Statistics is currently edited by Jun Shao

More articles in Journal of Nonparametric Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().