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Robust Hierarchical Bayes Estimation of Small Area Characteristics in the Presence of Covariates and Outliers

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  • Datta, G. S.
  • Lahiri, P.

Abstract

A robust hierarchical Bayes method is developed to smooth small area means when a number of covariates are available. The method is particularly suited when one or more outliers are present in the data. It is well known that the regular Bayes estimators of small. area means, under normal prior distribution, perform poorly in presence of even one extreme observation. In this case the Bayes estimators collapse to the direct survey estimators. This paper introduces a general theory for robust hierarchical Bayes estimation procedure using a fairly rich class of scale mixtures of normal prior distributions. To retain maximum benefit from combining information from related sources, we suggest to use Cauchy prior distribution for the outlying areas and an appropriate scale mixture of normal prior whose tail is lighter than the Cauchy prior for the rest of the areas. It is shown that, unlike the hierarchical Bayes estimator under a normal prior, our estimator has more protection against outlying observations.

Suggested Citation

  • Datta, G. S. & Lahiri, P., 1995. "Robust Hierarchical Bayes Estimation of Small Area Characteristics in the Presence of Covariates and Outliers," Journal of Multivariate Analysis, Elsevier, vol. 54(2), pages 310-328, August.
    • Handle: RePEc:eee:jmvana:v:54:y:1995:i:2:p:310-328
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    Citations

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    Cited by:

    1. Nikos Tzavidis & Li‐Chun Zhang & Angela Luna & Timo Schmid & Natalia Rojas‐Perilla, 2018. "From start to finish: a framework for the production of small area official statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 927-979, October.
    2. Harm Jan Boonstra & Jan van den Brakel & Sumonkanti Das, 2021. "Multilevel time series modelling of mobility trends in the Netherlands for small domains," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 985-1007, July.
    3. Tzavidis, Nikos & Zhang, Li-Chun & Luna Hernandez, Angela & Schmid, Timo & Rojas-Perilla, Natalia, 2016. "From start to finish: A framework for the production of small area official statistics," Discussion Papers 2016/13, Free University Berlin, School of Business & Economics.
    4. Unnikrishnan, N.K., 2010. "Bayesian analysis for outliers in survey sampling," Computational Statistics & Data Analysis, Elsevier, vol. 54(8), pages 1962-1974, August.
    5. Chakraborty Adrijo & Datta Gauri Sankar & Mandal Abhyuday, 2016. "A Two-Component Normal Mixture Alternative to the Fay-Herriot Model," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 67-90, March.
    6. Adrijo Chakraborty & Gauri Sankar Datta & Abhyuday Mandal, 2016. "A Two-Component Normal Mixture Alternative To The Fay-Herriot Model," Statistics in Transition New Series, Polish Statistical Association, vol. 17(1), pages 67-90, March.
    7. repec:csb:stintr:v:17:y:2016:i:1:p:67-90 is not listed on IDEAS
    8. Fernando A. S. Moura & André Felipe Neves & Denise Britz do N. Silva, 2017. "Small area models for skewed Brazilian business survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 1039-1055, October.
    9. Charlotte Articus & Jan Pablo Burgard, 2014. "A Finite Mixture Fay Herriot-type model for estimating regional rental prices in Germany," Research Papers in Economics 2014-14, University of Trier, Department of Economics.

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