Ordinary least squares and Stein-rule predictions in regression models under inclusion of some superfluous variables
Author:
Publisher
Springer Science and Business Media LLC
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Link
http://link.springer.com/content/pdf/10.1007/BF02926587.pdf
Reference4 articles.
1. Dube, M., V.K. Srivastava, H. Toutenburg and P. Wijekoon (1991) SteinruleEstimator under Inclusion of Superfluous variables in Linear Regression Models” Communications in Statistics—Theory and Methods 20(7) 2009–2022.
2. Srivastava, V.K. and A.Chaturvedi (1986) “A Necessary and Sufficient condition for the Dominance of an Improved Family of Estimators in Linear Regression Models”, Economics Letters, 20, 345–349.
3. Srivastava, V.K. and M. Dube (1993). “Properties of the Ordinary least squares and Stein- rule Predictions in Linear Regression Models with Proxy Variables” Statistical Papers, 34, 27–41.
4. Trenkler, G. and H. Toutenberg (1992) “Pre—test Procedures and Forecasting in the Regression Model under Restrictions” Journal of Statistical Planning and Inference, 30, 250–1.
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