Small area prediction of proportions and counts under a spatial Poisson mixed model

Miguel Boubeta, María José Lombardía () and Domingo Morales
Additional contact information
Miguel Boubeta: Universidade da Coruña
María José Lombardía: Universidade da Coruña
Domingo Morales: Universidad Miguel Hernández de Elche

Statistical Methods & Applications, 2024, vol. 33, issue 4, No 8, 1193-1215

Abstract: Abstract This paper introduces an area-level Poisson mixed model with SAR(1) spatially correlated random effects. Small area predictors of proportions and counts are derived from the new model and the corresponding mean squared errors are estimated by parametric bootstrap. The behaviour of the introduced predictors is empirically investigated by running model-based simulation experiments. An application to real data from the Spanish living conditions survey of Galicia (Spain) is given. The target is the estimation of domain proportions of women under the poverty line.

Keywords: Small area estimation; Area-level models; Spatial correlation; Count data; Bootstrap; Living conditions survey; poverty proportion; 62E30; 62J12 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10260-023-00729-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:stmapp:v:33:y:2024:i:4:d:10.1007_s10260-023-00729-7

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2

DOI: 10.1007/s10260-023-00729-7

Access Statistics for this article

Statistical Methods & Applications is currently edited by Tommaso Proietti

More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().