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Small area estimation for semicontinuous data

Journal Article


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Abstract


  • Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linearmixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study.

Publication Date


  • 2016

Citation


  • Chandra, H. & Chambers, R. L. (2016). Small area estimation for semicontinuous data. Biometrical Journal: journal of mathematical methods in biosciences, 58 (2), 303-319.

Scopus Eid


  • 2-s2.0-84959355088

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5039&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4020

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 303

End Page


  • 319

Volume


  • 58

Issue


  • 2

Place Of Publication


  • Germany

Abstract


  • Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linearmixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study.

Publication Date


  • 2016

Citation


  • Chandra, H. & Chambers, R. L. (2016). Small area estimation for semicontinuous data. Biometrical Journal: journal of mathematical methods in biosciences, 58 (2), 303-319.

Scopus Eid


  • 2-s2.0-84959355088

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=5039&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/4020

Has Global Citation Frequency


Number Of Pages


  • 16

Start Page


  • 303

End Page


  • 319

Volume


  • 58

Issue


  • 2

Place Of Publication


  • Germany