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Prediction of Finite Population Total in Measurement Error Models

  • Hyang Mi Kim
  • A. K. Md. Ehsanes Saleh

Measurement error regression models are different from classical regression models mainly that the covariates are measured with errors. This paper deals with the prediction of finite population total based on regression models with measurement errors. General treatment of regression problems with measurement errors is considered in the pioneering book by Fuller (1987) and Cheng and Van Ness (1999). Later Sprent (1966) proposed methods based on generalized least-squares approach for estimating the regression coefficients. Lindley (1966) and Lindley and Sayad (1968) pioneered Bayesian approach to the problem. Further, contribution in Bayesian approach have been made by Zellner (1971) and Reilly and Patino-Leal (1981). Fuller (1975) points out not much research is done for problems in finite population with measurement error models. However, Bolfarine (1991) investigated the problem of predictors for finite population with errors in variable models. Recently, Kim and Saleh (2002, 2003, 2005) pioneered the application of preliminary test and shrinkage estimation methodology in measurement error models. Recent book of Saleh (2006) presents an overview on the theory of preliminary test and Shrinkage estimators. This paper contains the application of these ideas for the prediction of finite population totals using simple linear model with measurement errors.

Keywords

Ordinary Little Square Royal Statistical Society Finite Population Simple Linear Model Measurement Error Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2008

Authors and Affiliations

  • Hyang Mi Kim
    • 1
  • A. K. Md. Ehsanes Saleh
    • 2
  1. 1.Department of Mathematics and Statistics, Division of Statistics and Actuarial ScienceUniversity of CalgaryCalgaryCanada
  2. 2.School of Mathematics and StatisticsCarleton UniversityOttawaCanada

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