Improved Estimation in Measurement Error Models Through Stein Rule Procedure
AbstractThis paper examines the role of Stein estimation in a linear ultrastructural form of the measurement errors model. It is demonstrated that the application of Stein rule estimation to the matrix of true values of regressors leads to the overcoming of the inconsistency of the least squares procedure and yields consistent estimators of regression coefficients. A further application may improve the efficiency properties of the estimators of regression coefficients. It is observed that the proposed family of estimators under some constraint on the characterizing scalar dominates the conventional consistent estimator with respect to the criterion of asymptotic risk under a specific quadratic loss function. Then the problem of prediction of the values of the study variable within the sample is considered, and it is found that the predictors based on the proposed family of estimators are always more efficient than the predictors based on the conventional estimator according to asymptotic predictive mean squared error criterion, although both are biased.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 67 (1998)
Issue (Month): 1 (October)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Srivastava, Anil K. & Shalabh, 1997. "A new property of Stein procedure in measurement error model," Statistics & Probability Letters, Elsevier, vol. 32(3), pages 231-234, March.
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- Zheng, Z., 1986. "On estimation of matrix of normal mean," Journal of Multivariate Analysis, Elsevier, vol. 18(1), pages 70-82, February.
- Liang, Hua & Song, Weixing, 2009. "Improved estimation in multiple linear regression models with measurement error and general constraint," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 726-741, April.
- Kim, H.M. & Saleh, A.K.Md.Ehsanes, 2005. "Improved estimation of regression parameters in measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 273-300, August.
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