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M-quantile models with application to poverty mapping

Journal Article


Abstract


  • Over the last decade there has been growing demand for estimates of

    population characteristics at small area level. Unfortunately, cost constraints in the

    design of sample surveys lead to small sample sizes within these areas and as a result

    direct estimation, using only the survey data, is inappropriate since it yields estimates

    with unacceptable levels of precision. Small area models are designed to tackle the

    small sample size problem. The most popular class ofmodels for small area estimation

    is random effects models that include random area effects to account for between area

    variations. However, such models also depend on strong distributional assumptions,

    require a formal specification of the random part of the model and do not easily allow

    for outlier robust inference. An alternative approach to small area estimation that

    is based on the use of M-quantile models was recently proposed by Chambers and

    Tzavidis (Biometrika 93(2):255-268, 2006) and Tzavidis and Chambers (Robust prediction

    of small area means and distributions.Working paper, 2007).Unlike traditional

    random effects models, M-quantile models do not depend on strong distributional

Authors


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

Publication Date


  • 2008

Citation


  • Tzavidis, N., Salvati, N., Pratesi, M. & Chambers, R. L. (2008). M-quantile models with application to poverty mapping. Statistical Methods and Applications, 17 (3), 393-411.

Scopus Eid


  • 2-s2.0-47249141286

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1677

Number Of Pages


  • 18

Start Page


  • 393

End Page


  • 411

Volume


  • 17

Issue


  • 3

Place Of Publication


  • http://www.springerlink.com/content/l7t626634811/

Abstract


  • Over the last decade there has been growing demand for estimates of

    population characteristics at small area level. Unfortunately, cost constraints in the

    design of sample surveys lead to small sample sizes within these areas and as a result

    direct estimation, using only the survey data, is inappropriate since it yields estimates

    with unacceptable levels of precision. Small area models are designed to tackle the

    small sample size problem. The most popular class ofmodels for small area estimation

    is random effects models that include random area effects to account for between area

    variations. However, such models also depend on strong distributional assumptions,

    require a formal specification of the random part of the model and do not easily allow

    for outlier robust inference. An alternative approach to small area estimation that

    is based on the use of M-quantile models was recently proposed by Chambers and

    Tzavidis (Biometrika 93(2):255-268, 2006) and Tzavidis and Chambers (Robust prediction

    of small area means and distributions.Working paper, 2007).Unlike traditional

    random effects models, M-quantile models do not depend on strong distributional

Authors


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

Publication Date


  • 2008

Citation


  • Tzavidis, N., Salvati, N., Pratesi, M. & Chambers, R. L. (2008). M-quantile models with application to poverty mapping. Statistical Methods and Applications, 17 (3), 393-411.

Scopus Eid


  • 2-s2.0-47249141286

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1677

Number Of Pages


  • 18

Start Page


  • 393

End Page


  • 411

Volume


  • 17

Issue


  • 3

Place Of Publication


  • http://www.springerlink.com/content/l7t626634811/