Abstract
This paper extends the balanced loss function to a more general set up. The ordinary least squares and Stein-rule estimators are exposed to this general loss function with quadratic loss structure in a linear regression model. Their risks are derived when the disturbances in the linear regression model are not necessarily normally distributed. The dominance of ordinary least squares and Stein-rule estimators over each other and the effect of departure from normality assumption of disturbances on the risk property is studied.
Item Type: | Paper |
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Form of publication: | Submitted Version |
Keywords: | Linear regression model, Stein-rule estimator, ordinary least squares estimator, balanced loss function, non-normal disturbances |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
URN: | urn:nbn:de:bvb:19-epub-2080-5 |
Language: | English |
Item ID: | 2080 |
Date Deposited: | 11. Dec 2007, 10:24 |
Last Modified: | 04. Nov 2020, 12:46 |