This session contains three contributions of robust methods to small area estimation. The talks give methodological developments and applications related to estimation of quantiles based on Fay-Herriot models, new model-based method that minimizes density power divergences and new bias calibration approaches for robust estimation of inequality indices.
Central banks and politics often use robust measures like quantiles in order to describe the distribution of income or wealth in a country. However, estimates on a disaggregated level are rarely reported due to small sample sizes and following large variances. Small area estimation is one way to handle this issue but standard approaches are […]
Empirical Bayes estimators are widely used to provide indirect and model-based estimates of means in small areas. The most common model is a two-stage normal hierarchical model called Fay-Herriot model. However, due to the normality assumption, it might be highly influenced by the presence of outliers. In this talk, we propose a simple modification of […]
Today the availability of rich sample surveys provides a ground for researchers and policy makers to pursue more ambitious objectives. This information in line with auxiliary data coming through administrative channels are used for a better prediction/estimation of social and economic indices, e.g. inequality or poverty measures, that can help to determine more precisely their […]