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Contextual effects in modeling for small domain estimation

Conference Paper


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Abstract


  • Many different Small Area Estimation (SAE) methods have been proposed to overcome the challenge of finding

    reliable estimates for small domains. Often, the required data for various research purposes are available at different

    levels of aggregation. Based on the available data, individual-level or aggregated-level models are used in SAE.

    However, parameter estimates obtained from individual and aggregated level analysis may be different, in practice.

    This may happen due to some substantial contextual or area-level effects in the covariates which may be misspecified

    in individual-level analysis. If small area models are going to be interpretable in practice, possible contextual

    effects should be included. Ignoring these effects leads to misleading results. In this paper, synthetic estimators and

    Empirical Best Linear Unbiased Predictors (EBLUPs) are evaluated in SAE based on different levels of linear mixed

    models. Using a numerical simulation study, the key role of contextual effects is examined for model selection in

    SAE.

    Key words: Contextual Effect; EBLUP; Small Area Estimation; Synthetic Estimator.

Publication Date


  • 2011

Citation


  • Namazi Rad, M. & Steel, D. (2011). Contextual effects in modeling for small domain estimation. In E. Beh, L. Park & K. Russell (Eds.), Proceedings of the 4th Applied Statistics Education and Research Collaboration (ASEARC) Conference (pp. 12-14). Wollongong: University of Wollongong.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1049&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/44

Start Page


  • 12

End Page


  • 14

Place Of Publication


  • http://www.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow096236.pdf

Abstract


  • Many different Small Area Estimation (SAE) methods have been proposed to overcome the challenge of finding

    reliable estimates for small domains. Often, the required data for various research purposes are available at different

    levels of aggregation. Based on the available data, individual-level or aggregated-level models are used in SAE.

    However, parameter estimates obtained from individual and aggregated level analysis may be different, in practice.

    This may happen due to some substantial contextual or area-level effects in the covariates which may be misspecified

    in individual-level analysis. If small area models are going to be interpretable in practice, possible contextual

    effects should be included. Ignoring these effects leads to misleading results. In this paper, synthetic estimators and

    Empirical Best Linear Unbiased Predictors (EBLUPs) are evaluated in SAE based on different levels of linear mixed

    models. Using a numerical simulation study, the key role of contextual effects is examined for model selection in

    SAE.

    Key words: Contextual Effect; EBLUP; Small Area Estimation; Synthetic Estimator.

Publication Date


  • 2011

Citation


  • Namazi Rad, M. & Steel, D. (2011). Contextual effects in modeling for small domain estimation. In E. Beh, L. Park & K. Russell (Eds.), Proceedings of the 4th Applied Statistics Education and Research Collaboration (ASEARC) Conference (pp. 12-14). Wollongong: University of Wollongong.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1049&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/44

Start Page


  • 12

End Page


  • 14

Place Of Publication


  • http://www.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow096236.pdf