Double k-class estimators in regression models with non-spherical disturbances

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)226-250
Journal / PublicationJournal of Multivariate Analysis
Issue number2
Publication statusPublished - 2001


In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized least squares and Stein-rule estimators using the mean squared error matrix and risk under quadratic loss criteria. A Monte-Carlo experiment investigates the finite sample behaviour of the proposed family of estimators. © 2001 Academic Press.

Research Area(s)

  • Bias, Dominance, Large sample asymptotic, Mean squared error, Monte-Carlo simulation, Quadratic loss, Risk