Arbeitspapier
Robust small area estimation under spatial non-stationarity
Geographically weighted small area methods have been studied in literature for small area estimation. Although these approaches are useful for the estimation of small area means efficiently under strict parametric assumptions, they can be very sensitive to outliers in the data. In this paper, we propose a robust extension of the geographically weighted empirical best linear unbiased predictor (GWEBLUP). In particular, we introduce robust projective and predictive small area estimators under spatial non-stationarity. Mean squared error estimation is performed by two different analytic approaches that account for the spatial structure in the data. The results from the model-based simulations indicate that the proposed approach may lead to gains in terms of efficiency. Finally, the methodology is demonstrated in an illustrative application for estimating the average total cash costs for farms in Australia.
- Sprache
-
Englisch
- Erschienen in
-
Series: Diskussionsbeiträge ; No. 2016/5
- Klassifikation
-
Wirtschaft
- Thema
-
bias correction
geographical weighted regression
mean squared error
model-based simulation
spatial statistics
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Baldermann, Claudia
Salvati, Nicola
Schmid, Timo
- Ereignis
-
Veröffentlichung
- (wer)
-
Freie Universität Berlin, Fachbereich Wirtschaftswissenschaft
- (wo)
-
Berlin
- (wann)
-
2016
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Baldermann, Claudia
- Salvati, Nicola
- Schmid, Timo
- Freie Universität Berlin, Fachbereich Wirtschaftswissenschaft
Entstanden
- 2016
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