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Modelling Group Heterogeneity for Small Area Estimation Using M-Quantiles

Journal Article


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Abstract


  • Small area estimation typically requires model-based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M-quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.

Publication Date


  • 2018

Citation


  • Dawber, J. & Chambers, R. (2018). Modelling Group Heterogeneity for Small Area Estimation Using M-Quantiles. International Statistical Review, Online First 1-14.

Scopus Eid


  • 2-s2.0-85053475945

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2826&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1824

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Volume


  • Online First

Place Of Publication


  • United Kingdom

Abstract


  • Small area estimation typically requires model-based methods that depend on isolating the contribution to overall population heterogeneity associated with group (i.e. small area) membership. One way of doing this is via random effects models with latent group effects. Alternatively, one can use an M-quantile ensemble model that assigns indices to sampled individuals characterising their contribution to overall sample heterogeneity. These indices are then aggregated to form group effects. The aim of this article is to contrast these two approaches to characterising group effects and to illustrate them in the context of small area estimation. In doing so, we consider a range of different data types, including continuous data, count data and binary response data.

Publication Date


  • 2018

Citation


  • Dawber, J. & Chambers, R. (2018). Modelling Group Heterogeneity for Small Area Estimation Using M-Quantiles. International Statistical Review, Online First 1-14.

Scopus Eid


  • 2-s2.0-85053475945

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2826&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/1824

Number Of Pages


  • 13

Start Page


  • 1

End Page


  • 14

Volume


  • Online First

Place Of Publication


  • United Kingdom