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Bias Calibration for Robust Estimation in Small Areas

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Setareh Ranjbar
Elvezio Ronchetti
Stefan Sperlich
Published in
Robust and Multivariate Statistical Methods
Publisher
Springer International Publishing

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Abstract

The chapter delves into the critical issue of bias calibration for robust estimation in small areas, an area of research pioneered by Dave Tyler. It explores the challenges posed by the sensitivity of small area estimation (SAE) techniques to outliers and the need for robust estimators. The authors focus on mixed effects models (MEM) with area-specific random effects and discuss existing robust methods like the robust version of the EBLUP (REBLUP) and the M-quantile (MQ) approach. The chapter introduces two innovative ideas for calibrating non-linear parameter estimators: asymmetric calibration to reflect skewed data generating processes and linearization using the Influence Function (IF) followed by calibration. These methods aim to reduce bias and improve efficiency, as demonstrated through simulations and an application to estimate the Gini coefficient in Tuscany, Italy. The chapter concludes by emphasizing the practical benefits of asymmetric bias calibration and the potential for future research in this area.
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previous chapter Conditional Distribution-Based Downweighting for Robust Estimation of Logistic Regression Models
next chapter The Diverging Definition of Robustness in Statistics and Computer Vision
Title
Bias Calibration for Robust Estimation in Small Areas
Authors
Setareh Ranjbar
Elvezio Ronchetti
Stefan Sperlich
Copyright Year
2023
Book
Robust and Multivariate Statistical Methods

Print ISBN: 978-3-031-22686-1

Electronic ISBN: 978-3-031-22687-8

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-22687-8_17
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