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Bayesian Analysis in Statistics and Econometrics pp 107–125Cite as

A Hierarchical Bayes Approach to Small Area Estimation with Auxiliary Information

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 75))

Abstract

Small area estimation is becoming increasingly popular in survey sampling. Agencies of the Federal Government have been involved in obtaining estimates of population counts, unemployment rates, per capita income, crop yields and so forth for state and local government areas. In many instances, only a few samples are available from an individual area, and an estimate of a certain area mean, or simultaneous estimates of several area means can be improved by incorporating information from similar neighboring areas.

The present paper introduces a hierarchical Bayes model incorporating auxiliary information in the context of small area estimation. One advantage of the hierarchical Bayes procedure over a classical empirical Bayes procedure is that not only can we obtain good point estimates of the small area means, but also it is possible to obtain good estimates of their variability as well.

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Author information

Authors and Affiliations

  1. University of Florida, USA

    Malay Ghosh

  2. University of Nebraska, USA

    Parthasarathi Lahiri

Authors
  1. Malay Ghosh
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  2. Parthasarathi Lahiri
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Editor information

Editors and Affiliations

  1. Department of Statistics, The Ohio State University, Columbus, OH, 43210-1247, USA

    Prem K. Goel

  2. Economic Analysis Unit, Indian Statistical Institute, Bangalore, 8 Mile Mysole Road, R.V. College — P.O., Bangalore, 560059, India

    N. Sreenivas Iyengar

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© 1992 Springer-Verlag New York, Inc.

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Ghosh, M., Lahiri, P. (1992). A Hierarchical Bayes Approach to Small Area Estimation with Auxiliary Information. In: Goel, P.K., Iyengar, N.S. (eds) Bayesian Analysis in Statistics and Econometrics. Lecture Notes in Statistics, vol 75. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2944-5_6

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  • DOI: https://doi.org/10.1007/978-1-4612-2944-5_6

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97863-5

  • Online ISBN: 978-1-4612-2944-5

  • eBook Packages: Springer Book Archive

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