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Small area estimation under spatial nonstationarity

Journal Article


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. (C) 2012 Elsevier B.V. All rights reserved."

Authors


  •   Chandra, Hukum (external author)
  •   Salvati, Nicola (external author)
  •   Chambers, Raymond L.
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2012

Citation


  • Chandra, H., Salvati, N., Chambers, R. L. & Tzavidis, N. (2012). Small area estimation under spatial nonstationarity. Computational Statistics and Data Analysis, 56 (10), 2875-2888.

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2112

Number Of Pages


  • 13

Start Page


  • 2875

End Page


  • 2888

Volume


  • 56

Issue


  • 10

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. (C) 2012 Elsevier B.V. All rights reserved."

Authors


  •   Chandra, Hukum (external author)
  •   Salvati, Nicola (external author)
  •   Chambers, Raymond L.
  •   Tzavidis, Nikos (external author)

Publication Date


  • 2012

Citation


  • Chandra, H., Salvati, N., Chambers, R. L. & Tzavidis, N. (2012). Small area estimation under spatial nonstationarity. Computational Statistics and Data Analysis, 56 (10), 2875-2888.

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2112

Number Of Pages


  • 13

Start Page


  • 2875

End Page


  • 2888

Volume


  • 56

Issue


  • 10