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Small area estimation of proportions in business surveys

Journal Article


Abstract


  • Binary data are often of interest in business surveys, particularly when the aim is to characterize grouping

    in the businesses making up the survey population. When small area estimates are required for such binary

    data, use of standard estimation methods based on linear mixed models (LMMs) becomes problematic.We

    explore two model-based techniques of small area estimation for small area proportions, the empirical best

    predictor (EBP) under a generalized linear mixed model and the model-based direct estimator (MBDE)

    under a population-level LMM. Our empirical results show that both the MBDE and the EBP perform well.

    The EBP is a computationally intensive method, whereas the MBDE is easy to implement. In case of model

    misspecification, the MBDE also appears to be more robust. The mean-squared error (MSE) estimation of

    MBDE is simple and straightforward, which is in contrast to the complicated MSE estimation for the EBP.

Authors


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

Publication Date


  • 2012

Citation


  • Chandra, H., Chambers, R. L. & Salvati, N. (2012). Small area estimation of proportions in business surveys. Journal of Statistical Computation and Simulation, 82 (6), 783-795.

Scopus Eid


  • 2-s2.0-84861443439

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2018

Number Of Pages


  • 12

Start Page


  • 783

End Page


  • 795

Volume


  • 82

Issue


  • 6

Abstract


  • Binary data are often of interest in business surveys, particularly when the aim is to characterize grouping

    in the businesses making up the survey population. When small area estimates are required for such binary

    data, use of standard estimation methods based on linear mixed models (LMMs) becomes problematic.We

    explore two model-based techniques of small area estimation for small area proportions, the empirical best

    predictor (EBP) under a generalized linear mixed model and the model-based direct estimator (MBDE)

    under a population-level LMM. Our empirical results show that both the MBDE and the EBP perform well.

    The EBP is a computationally intensive method, whereas the MBDE is easy to implement. In case of model

    misspecification, the MBDE also appears to be more robust. The mean-squared error (MSE) estimation of

    MBDE is simple and straightforward, which is in contrast to the complicated MSE estimation for the EBP.

Authors


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

Publication Date


  • 2012

Citation


  • Chandra, H., Chambers, R. L. & Salvati, N. (2012). Small area estimation of proportions in business surveys. Journal of Statistical Computation and Simulation, 82 (6), 783-795.

Scopus Eid


  • 2-s2.0-84861443439

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2018

Number Of Pages


  • 12

Start Page


  • 783

End Page


  • 795

Volume


  • 82

Issue


  • 6