Outlier robust small domain estimation via bias correction and robust bootstrapping

G. Bertarelli (), R. Chambers () and N. Salvati ()
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G. Bertarelli: University of Pisa
R. Chambers: University of Wollongong
N. Salvati: University of Pisa

Statistical Methods & Applications, 2021, vol. 30, issue 1, No 14, 357 pages

Abstract: Abstract 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.

Keywords: M-quantile regression; Small area estimation; Robust estimation; Survey sampling theory; Resampling methods (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10260-020-00514-w

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