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Small area estimation via M-quantile geographically weighted regression

Journal Article


Abstract


  • The effective use of spatial information, that is, the geographic locations

    of population units, in a regression model-based approach to small area estimation is

    an important practical issue. One approach for incorporating such spatial information

    in a small area regression model is via GeographicallyWeighted Regression (GWR).

    In GWR, the relationship between the outcome variable and the covariates is characterised

    by local rather than global parameters, where local is defined spatially. In

    this paper, we investigate GWR-based small area estimation under the M-quantile

    modelling approach. In particular, we specify an M-quantile GWR model that is a local

    model for the M-quantiles of the conditional distribution of the outcome variable

    given the covariates. This model is then used to define a bias-robust predictor of the

    small area characteristic of interest that also accounts for spatial association in the

    data. An important spin-off from applying the M-quantile GWR small area model is

    that it can potentially offer more efficient synthetic estimation for out of sample areas.

    We demonstrate the usefulness of this framework through both model-based as well

    as design-based simulations, with the latter based on a realistic survey data set. The

    paper concludes with an illustrative application that focuses on estimation of average

    levels of Acid Neutralising Capacity for lakes in the Northeast of the USA.

Authors


  •   Salvati, Nicola (external author)
  •   Tzavidis, Nikos (external author)
  •   Pratesi, Monica (external author)
  •   Chambers, Raymond L.

Publication Date


  • 2012

Published In


Citation


  • Salvati, N., Tzavidis, N., Pratesi, M. & Chambers, R. L. (2012). Small area estimation via M-quantile geographically weighted regression. Test, 21 (1), 1-28.

Scopus Eid


  • 2-s2.0-84858281160

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1993

Number Of Pages


  • 27

Start Page


  • 1

End Page


  • 28

Volume


  • 21

Issue


  • 1

Abstract


  • The effective use of spatial information, that is, the geographic locations

    of population units, in a regression model-based approach to small area estimation is

    an important practical issue. One approach for incorporating such spatial information

    in a small area regression model is via GeographicallyWeighted Regression (GWR).

    In GWR, the relationship between the outcome variable and the covariates is characterised

    by local rather than global parameters, where local is defined spatially. In

    this paper, we investigate GWR-based small area estimation under the M-quantile

    modelling approach. In particular, we specify an M-quantile GWR model that is a local

    model for the M-quantiles of the conditional distribution of the outcome variable

    given the covariates. This model is then used to define a bias-robust predictor of the

    small area characteristic of interest that also accounts for spatial association in the

    data. An important spin-off from applying the M-quantile GWR small area model is

    that it can potentially offer more efficient synthetic estimation for out of sample areas.

    We demonstrate the usefulness of this framework through both model-based as well

    as design-based simulations, with the latter based on a realistic survey data set. The

    paper concludes with an illustrative application that focuses on estimation of average

    levels of Acid Neutralising Capacity for lakes in the Northeast of the USA.

Authors


  •   Salvati, Nicola (external author)
  •   Tzavidis, Nikos (external author)
  •   Pratesi, Monica (external author)
  •   Chambers, Raymond L.

Publication Date


  • 2012

Published In


Citation


  • Salvati, N., Tzavidis, N., Pratesi, M. & Chambers, R. L. (2012). Small area estimation via M-quantile geographically weighted regression. Test, 21 (1), 1-28.

Scopus Eid


  • 2-s2.0-84858281160

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1993

Number Of Pages


  • 27

Start Page


  • 1

End Page


  • 28

Volume


  • 21

Issue


  • 1