Outlier Robust Small‐Area Estimation Under Spatial Correlation. (3rd February 2016)

• Record Type: Journal Article
• Authors: Schmid, Timo; Tzavidis, Nikos; Münnich, Ralf; Chambers, Ray
• Is Part Of: Scandinavian journal of statistics. Volume 43:Number 3(2016:Sep.)
• Page Start: 806
• Page End: 826
• Publication Date: 2016-02-03
• Subjects: bias correction -- business surveys -- projective and predictive estimators -- spatial correlation; Statistics -- Periodicals; 310
• DOI: 10.1111/sjos.12205
• Languages: English
• ISSNs: 0303-6898

Abstract

Abstract: Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small‐area estimation. Recently proposed outlier robust model‐based small‐area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small‐area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean‐squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.