1,722,089 research outputs found
Revision of Guidelines for the protection of social micro-data using individual risk methodology by S. Polettini and G. Seri (FP5 project "CASC" - Deliverable 1.2-D3)
No full text
Physical Properties and Acid Neutralisation Capacity of Incinerator Bottom Ash - Portland Cement Mixtures
No full textBayes factor for non-dominated statistical models
No full textThis paper deals with the definition of the Bayes factor (BF) for non-dominated statistical models, where the ordinary likelihood function is not defined. A general definition of BF is proposed, which also covers dominated models; its main properties are examined and its practical use discussed through some simple examples, aimed at illustrating the behaviour of the BF in non-standard problems. © 2001 Elsevier Science B.V
Threshold Uncertainty in Modelling Extreme Values by the Generalised Pareto Distribution
No full text
A Generalised Semiparametric Bayesian Fay–Herriot Model for Small Area Estimation Shrinking Both Means and Variances
No full textIn survey sampling, interest often lies in unplanned domains (or small areas), whose sample sizes may be too small to allow for accurate design-based inference. To improve the direct estimates by borrowing strength from similar domains, most small area methods rely on mixed effects regression models.
This contribution extends the well known Fay–Herriot model (Fay and Herriot, 1979) within a Bayesian approach in two directions. First, the default normality assumption for the random effects is replaced by a nonparametric specification using a Dirichlet process. Second, uncertainty on variances is explicitly intro- duced, recognizing the fact that they are actually estimated from survey data. The proposed approach shrinks variances as well as means, and accounts for all sources of uncertainty. Adopting a flexible model for the random effects allows to accommodate outliers and vary the borrowing of strength by identifying local neighbourhoods where the exchangeability assumption holds. Through applica- tion to real and simulated data, we investigate the performance of the proposed model in predicting the domain means under different distributional assumptions. We also focus on the construction of credible intervals for the area means, a topic that has received less attention in the literature. Frequentist properties such as mean squared prediction error (MSPE), coverage and interval length are inves- tigated. The experiments performed seem to indicate that inferences under the proposed model are characterised by smaller mean squared error than competing approaches; frequentist coverage of the credible intervals is close to nominal
- …
