Outlier robust small domain estimation via bias correction and robust bootstrapping

Authors

G Bertarelli
Raymond L. Chambers, University of WollongongFollow
N Salvati

RIS ID

141695

Publication Details

Bertarelli, G., Chambers, R. & Salvati, N. (2020). Outlier robust small domain estimation via bias correction and robust bootstrapping. Statistical Methods and Applications,

Abstract

2020, Springer-Verlag GmbH Germany, part of Springer Nature. Several methods have been devised to mitigate the effects of outlier values on survey estimates. If outliers are a concern for estimation of population quantities, it is even more necessary to pay attention to them in a small area estimation (SAE) context,where sample size is usually very small and the estimation in often model based. In this paper we set two goals: The first is to review recent developments in outlier robust SAE. In particular, we focus on the use of partial bias corrections when outlier robust fitted values under a working model generate biased predictions from sample data containing representative outliers.Then we propose an outlier robust bootstrap MSE estimator for M-quantile based small area predictors which considers a bounded-block-bootstrap approach. We illustrate these methods through model based and design based simulations and in the context of a particular survey data set that has many of the outlier characteristics that are observed in business surveys.