Robust small area prediction for counts
| Autor: | Emanuela Dreassi, Nikos Tzavidis, Nicola Salvati, M. Giovanna Ranalli, Ray Chambers |
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| Rok vydání: | 2014 |
| Předmět: |
Statistics and Probability
Generalized linear model robust inference Epidemiology Gaussian Health Status Poisson distribution M-quantile regression Generalized linear mixed model Sampling Studies symbols.namesake Health Information Management Robustness (computer science) Surveys and Questionnaires Statistics Econometrics Humans bootstrap generalized linear models health survey M-quantile regression non-normal outcomes robust inference Poisson Distribution bootstrap non-normal outcomes generalized linear models health survey Mathematics Aged Likelihood Functions Models Statistical Data application Random effects model Health Surveys Italy Health Care Surveys Sample Size Outlier symbols Regression Analysis Delivery of Health Care |
| Zdroj: | Statistical methods in medical research. 24(3) |
| ISSN: | 1477-0334 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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