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Robust small area prediction for counts

Robust small area prediction for counts
Robust small area prediction for counts
A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches
bootstrap, generalized linear models, health survey M-quantile regression, non-normal outcomes, robust inference
0962-2802
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Ranalli, M.G.
b5f1a69a-7b7d-4595-9db1-94428e7abe70
Salvati, N.
d1b7ebe3-afad-40fb-b32c-e748e344e922
Dreassi, E.
1077f49d-283d-4842-9a21-6f5289dc6c6a
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2
Tzavidis, Nikos
431ec55d-c147-466d-9c65-0f377b0c1f6a
Ranalli, M.G.
b5f1a69a-7b7d-4595-9db1-94428e7abe70
Salvati, N.
d1b7ebe3-afad-40fb-b32c-e748e344e922
Dreassi, E.
1077f49d-283d-4842-9a21-6f5289dc6c6a
Chambers, Ray
96331700-f45e-4483-a887-fef921888ff2

Tzavidis, Nikos, Ranalli, M.G., Salvati, N., Dreassi, E. and Chambers, Ray (2014) Robust small area prediction for counts. Statistical Methods in Medical Research. (doi:10.1177/0962280214520731).

Record type: Article

Abstract

A new semiparametric approach to model-based small area prediction for counts is proposed and used for estimating the average number of visits to physicians for Health Districts in Central Italy. The proposed small area predictor can be viewed as an outlier robust alternative to the more commonly used empirical plug-in predictor that is based on a Poisson generalized linear mixed model with Gaussian random effects. Results from the real data application and from a simulation experiment confirm that the proposed small area predictor has good robustness properties and in some cases can be more efficient than alternative small area approaches

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More information

Published date: 2 February 2014
Keywords: bootstrap, generalized linear models, health survey M-quantile regression, non-normal outcomes, robust inference
Organisations: Statistical Sciences Research Institute

Identifiers

Local EPrints ID: 361985
URI: http://eprints.soton.ac.uk/id/eprint/361985
ISSN: 0962-2802
PURE UUID: ab8d8246-4068-4709-a72b-578d81e47e7b
ORCID for Nikos Tzavidis: ORCID iD orcid.org/0000-0002-8413-8095

Catalogue record

Date deposited: 10 Feb 2014 14:30
Last modified: 15 Mar 2024 03:11

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Contributors

Author: Nikos Tzavidis ORCID iD
Author: M.G. Ranalli
Author: N. Salvati
Author: E. Dreassi
Author: Ray Chambers

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