Robust methods

Chair: Cristina Rueda

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.

Estimation of quantiles based on Fay-Herriot models

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 […]

Robust Empirical Bayes Small Area Estimation with Density Power Divergence

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 […]

A new bias calibration approach for robust inequality indices

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 […]