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.
Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, UK n.tzavidis@soton.ac.uk.
Nikos
N
Tzavidis
Dipartimento di Economia, Finanza e Statistica, Universtà degli Studi di Perugia, Perugia, Italy.
M Giovanna
MG
Ranalli
Dipartimento di Economia e Management, Università di Pisa, Pisa, Italy.
Nicola
N
Salvati
Dipartimento di Statistica, Informatica, Applicazioni (DiSIA), Università di Firenze, Firenze, Italy.
Emanuela
E
Dreassi
National Institute for Applied Statistics Research Australia, University of Wollongong, New South Wales, Australia.
Ray
R
Chambers
D000368:Aged; D003695:Delivery of Health Care / Q000706:statistics & numerical data; D019538:Health Care Surveys / Q000379:methods* / Q000706:statistics & numerical data; D006304:Health Status; D006306:Health Surveys / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D007558:Italy / Q000453:epidemiology; D016013:Likelihood Functions; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D018401:Sample Size; D012494:Sampling Studies; D011795:Surveys and Questionnaires
M-quantile regression; bootstrap; generalized linear models; health survey; non-normal outcomes; robust inference