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Outlier robust small-area estimation under spatial correlation

Journal Article


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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.

Authors


  •   Schmid, Timo (external author)
  •   Tzavidis, Nikos (external author)
  •   Munnich, Ralf (external author)
  •   Chambers, Raymond L.

Publication Date


  • 2016

Citation


  • Schmid, T., Tzavidis, N., Munnich, R. & Chambers, R. (2016). Outlier robust small-area estimation under spatial correlation. Scandinavian Journal of Statistics: theory and applications, 43 (3), 806-826.

Scopus Eid


  • 2-s2.0-84958719600

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6736&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5708

Number Of Pages


  • 20

Start Page


  • 806

End Page


  • 826

Volume


  • 43

Issue


  • 3

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.

Authors


  •   Schmid, Timo (external author)
  •   Tzavidis, Nikos (external author)
  •   Munnich, Ralf (external author)
  •   Chambers, Raymond L.

Publication Date


  • 2016

Citation


  • Schmid, T., Tzavidis, N., Munnich, R. & Chambers, R. (2016). Outlier robust small-area estimation under spatial correlation. Scandinavian Journal of Statistics: theory and applications, 43 (3), 806-826.

Scopus Eid


  • 2-s2.0-84958719600

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=6736&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5708

Number Of Pages


  • 20

Start Page


  • 806

End Page


  • 826

Volume


  • 43

Issue


  • 3