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Optimal Generalized Biased Estimator in Linear Regression Model
Postgraduate Institute of Science, University of Peradeniya, Peradeniya, Sri Lanka
Department of Statistics & Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
Department of Mathematics and Statistics, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
- 1 Postgraduate Institute of Science, University of Peradeniya, Peradeniya, Sri Lanka
- 2 Department of Statistics & Computer Science, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka
- 3 Department of Mathematics and Statistics, Faculty of Science, University of Jaffna, Jaffna, Sri Lanka
Open Journal of Statistics·Volume 05 (2015)·Pages 403–411·Published 23 July 2015·DOI10.4236/ojs.2015.55042
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Abstract
The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
KeywordsMulticollinearityBiased EstimatorGeneralized Optimal EstimatorScalar Mean Square Error
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