Improved estimation of regression parameters in measurement error models

H.M. Kim and A.K.Md.Ehsanes Saleh

Journal of Multivariate Analysis, 2005, vol. 95, issue 2, 273-300

Abstract: The problem of simultaneous estimation of the regression parameters in a multiple regression model with measurement errors is considered when it is suspected that the regression parameter vector may be the null-vector with some degree of uncertainty. In this regard, we propose two sets of four estimators, namely, (i) the unrestricted estimator, (ii) the preliminary test estimator, (iii) the Stein-type estimator and (iv) the postive-rule Stein-type estimator. In an asymptotic setup, properties of these estimators are studied based on asymptotic distributional bias, MSE matrices, and risks under a quadratic loss function. In addition to the asymptotic dominance of the Stein-type estimators, the paper contains discussion of dominating confidence sets based on the Stein-type estimation. Asymptotic analysis is considered based on a sequence of local alternatives to obtain the desired results.

Keywords: Linear; regression; Empirical; Bayes; Point; estimation; Confidence; regions (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (3)

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