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    Copula-based predictions in small area estimation

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    Grover_Kanika.pdf (461.3Kb)
    Date
    2018-07
    Author
    Grover, Kanika
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    Abstract
    Unit-level regression models are commonly used in small area estimation to obtain empirical best linear unbiased prediction of small area characteristics. A more flexible small area estimation model has been recently proposed using the linear regression to estimate the error terms and a multivariate exchangeable copula model to characterize the error distribution within each small area. In this work, we propose a likelihood framework to estimate the intra-class dependence of the multivariate exchangeable copula for the empirical best unbiased prediction (EBUP) of small area means. Further, we propose a bootstrap approach for both parametric and semi-parametric methods to obtain a nearly unbiased estimate of the mean squared prediction error (MSPE) of the EBUP of small area means. Performance of the proposed method is evaluated through a simulation study and also by a real data application.
    URI
    http://hdl.handle.net/1993/33183
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    • FGS - Electronic Theses and Practica [25522]

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