Double k-Class Estimators in Regression Models with Non-spherical Disturbances

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...

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Bibliographic Details
Published in:Journal of multivariate analysis 2001-11, Vol.79 (2), p.226-250
Main Authors: Wan, Alan T.K., Chaturvedi, Anoop
Format: Article
Language:English
Subjects:
bias
bias dominance large sample asymptotic quadratic loss mean squared error Monte-Carlo simulation risk
Distribution theory
dominance
Exact sciences and technology
large sample asymptotic
Mathematics
mean squared error
Monte-Carlo simulation
Probability and statistics
quadratic loss
risk
Sciences and techniques of general use
Statistics
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Summary: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.
ISSN:0047-259X
1095-7243
DOI:10.1006/jmva.2000.1963