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Small area estimation in practice an applocation to agricultural business survey data

Journal Article


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Abstract


  • This paper describes an application of small area estimation (SAbl to agricultural business survey data. Both well known

    small area estimators, such as the empirical best linear unbiased predictor (EBLUP), and more recently proposed small area

    estimators, for example, tile M-quanlile, the robust EBLUP aml!he Model Ansed Direct estimators arc considered. Mean squared

    error estimation is discussed. Using a real agricultural business survey dataset, we place emphasis on model diagnostics for

    specifying the small area working model, on diagnostic measures for validating the reliability of direct and indirect (modelbased)

    small area e.~timators i1nd on providing practical guidelines to the prospective user of small area estimation techniques.

Authors


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

Publication Date


  • 2012

Citation


  • Tzavidis, N., Chambers, R. L., Salvati, N. & Chandra, H. (2012). Small area estimation in practice an applocation to agricultural business survey data. Journal of the Indian Society of Agricultural Statistics, 66 (1), 213-228.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1764&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/758

Number Of Pages


  • 15

Start Page


  • 213

End Page


  • 228

Volume


  • 66

Issue


  • 1

Abstract


  • This paper describes an application of small area estimation (SAbl to agricultural business survey data. Both well known

    small area estimators, such as the empirical best linear unbiased predictor (EBLUP), and more recently proposed small area

    estimators, for example, tile M-quanlile, the robust EBLUP aml!he Model Ansed Direct estimators arc considered. Mean squared

    error estimation is discussed. Using a real agricultural business survey dataset, we place emphasis on model diagnostics for

    specifying the small area working model, on diagnostic measures for validating the reliability of direct and indirect (modelbased)

    small area e.~timators i1nd on providing practical guidelines to the prospective user of small area estimation techniques.

Authors


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

Publication Date


  • 2012

Citation


  • Tzavidis, N., Chambers, R. L., Salvati, N. & Chandra, H. (2012). Small area estimation in practice an applocation to agricultural business survey data. Journal of the Indian Society of Agricultural Statistics, 66 (1), 213-228.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1764&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/758

Number Of Pages


  • 15

Start Page


  • 213

End Page


  • 228

Volume


  • 66

Issue


  • 1