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Small area estimation under spatial nonstationarity

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Author Info

  • Chandra, Hukum
  • Salvati, Nicola
  • Chambers, Ray
  • Tzavidis, Nikos
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    Abstract

    A geographical weighted empirical best linear unbiased predictor (GWEBLUP) for a small area average is proposed, and an estimator of its conditional mean squared error is developed. The popular empirical best linear unbiased predictor under the linear mixed model is obtained as a special case of the GWEBLUP. Empirical results using both model-based and design-based simulations, with the latter based on two real data sets, show that the GWEBLUP predictor can lead to efficiency gains when spatial nonstationarity is present in the data. A practical gain from using the GWEBLUP is in small area estimation for out of sample areas. In this case the efficient use of geographical information can potentially improve upon conventional synthetic estimation.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312000734
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 10 ()
    Pages: 2875-2888

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:10:p:2875-2888

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Borrowing strength over space; Geographical weighted regression; Out of sample small area estimation; Spatial analysis;

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