Robust small area prediction for counts

Authors

Nikos Tzavidis, University of Southampton
M. Giovanna Ranalli, Universta degli Studi di Perugia
Nicola Salvati, University of Pisa
Emanuela Dreassi, Universita Degli Studi Di Firenze
Raymond L. Chambers, University of WollongongFollow

RIS ID

100477

Publication Details

Tzavidis, N., Ranalli, M., Salvati, N., Dreassi, E. & Chambers, R. (2015). Robust small area prediction for counts. Statistical Methods in Medical Research, 24 (3), 373-395.

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.