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Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles

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  • James Dawber
  • Raymond Chambers

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.

Suggested Citation

  • James Dawber & Raymond Chambers, 2019. "Modelling Group Heterogeneity for Small Area Estimation Using M‐Quantiles," International Statistical Review, International Statistical Institute, vol. 87(S1), pages 50-63, May.
    • Handle: RePEc:bla:istatr:v:87:y:2019:i:s1:p:s50-s63
    DOI: 10.1111/insr.12284
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    Cited by:

    1. Collin Philipps, 2022. "Interpreting Expectiles," Working Papers 2022-01, Department of Economics and Geosciences, US Air Force Academy.
    2. Balgobin Nandram, 2021. "A Bayesian Approach to Linking a Survey and a Census via Small Areas," Stats, MDPI, vol. 4(2), pages 1-20, June.

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