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An area-specific stick breaking process for spatial data

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  • Mahdi Hosseinpouri

    (Tarbiat Modares University)

  • Majid Jafari Khaledi

    (Tarbiat Modares University)

Abstract

Most of the existing Bayesian nonparametric models for spatial areal data assume that the neighborhood structures are known, however in practice this assumption may not hold. In this paper, we develop an area-specific stick breaking process for distributions of random effects with the spatially-dependent weights arising from the block averaging of underlying continuous surfaces. We show that this prior, which does not depend on specifying neighboring schemes, is noticeably flexible in effectively capturing heterogeneity in spatial dependency across areas. We illustrate the methodology with a dataset involving expenditure credit of 31 provinces of Iran.

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  • Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:1:d:10.1007_s00362-016-0833-0
    DOI: 10.1007/s00362-016-0833-0
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    Cited by:

    1. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.

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