Modelling group heterogeneity for small area estimation using M-quantiles
Modelling group heterogeneity for small area estimation using M-quantiles
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.
M-quantile regression, random effects model, small area estimation
S50-S63
Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6
Chambers, Raymond
68685a02-e1d0-4143-b5d4-0e91ff0e3d02
May 2019
Dawber, James
85c7c036-2ae3-4c57-a8b3-9f5223cd4da6
Chambers, Raymond
68685a02-e1d0-4143-b5d4-0e91ff0e3d02
Dawber, James and Chambers, Raymond
(2019)
Modelling group heterogeneity for small area estimation using M-quantiles.
International Statistical Review, 87 (S1), .
(doi:10.1111/insr.12284).
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.
Text
MQreview_ISR FINAL_eps_plots_REVISED2
- Accepted Manuscript
More information
Accepted/In Press date: 31 July 2018
e-pub ahead of print date: 16 September 2018
Published date: May 2019
Keywords:
M-quantile regression, random effects model, small area estimation
Identifiers
Local EPrints ID: 425052
URI: http://eprints.soton.ac.uk/id/eprint/425052
ISSN: 0306-7734
PURE UUID: d5b8a0d8-54ac-441c-a748-0bda07f0cceb
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Date deposited: 09 Oct 2018 16:30
Last modified: 16 Mar 2024 07:08
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Contributors
Author:
James Dawber
Author:
Raymond Chambers
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