Robust small area prediction for counts. (June 2015)

• Record Type: Journal Article
• Authors: Tzavidis, Nikos; Ranalli, M Giovanna; Salvati, Nicola; Dreassi, Emanuela; Chambers, Ray
• Other Names: Böhning Dankmar guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 24:Number 3(2015:Jun.)
• Page Start: 373
• Page End: 395
• Publication Date: 2015-06
• Subjects: bootstrap -- generalized linear models -- health survey -- M-quantile regression -- non-normal outcomes -- robust inference; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280214520731
• Languages: English
• ISSNs: 0962-2802

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.