Bias Calibration for Robust Estimation in Small Areas

Setareh Ranjbar (), Elvezio Ronchetti () and Stefan Sperlich ()
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Setareh Ranjbar: University of Lausanne, HEC
Elvezio Ronchetti: University of Geneva, Research Center for Statistics and Geneva School of Economics and Management
Stefan Sperlich: University of Geneva, Geneva School of Economics and Management

A chapter in Robust and Multivariate Statistical Methods, 2023, pp 365-394 from Springer

Abstract: Abstract It is well known that the existence of outliers in a sample can significantly affect the estimation of population parameters. Intuition suggests that this is even more the case in the context of small area estimation. If influential, outliers may heavily affect parameter estimates for areas in which they occur, especially when the domain-sample size is tiny. An obvious remedy is to use robust estimators but with the drawback of a potential bias. We compare different approaches, including some new ones, for bias calibration in this context. Among other findings, the simulations indicate that the new proposals can lead to more efficient estimators compared to existing methods. We conclude the study applying our estimators to obtain Gini coefficients in labor market areas of the Tuscany region of Italy. The new methods reveal a different picture than existing methods. We extend our ideas to predictions for non-sampled areas.

Keywords: Asymmetric Huber function; Non-linear population parameters; Robust estimation; Robust prediction; Small area estimation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-22687-8_17

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DOI: 10.1007/978-3-031-22687-8_17

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