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Statistics
A Journal of Theoretical and Applied Statistics
Volume 46, 2012 - Issue 6
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Original Articles

A stochastic restricted k–d class estimator

Hu Yang Department of Statistics and Actuarial Science, Chongqing University, 400030, Chongqing, People's Republic of China
&
Jibo Wu Department of Statistics and Actuarial Science, Chongqing University, 400030, Chongqing, People's Republic of ChinaCorrespondence[email protected]
Pages 759-766 | Received 09 Jun 2009, Accepted 13 Jan 2011, Published online: 30 Mar 2011

Citations (8)

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Read on this site (5)

Yong Li. (2020) A new stochastic mixed Liu estimator in linear regression model. Communications in Statistics - Theory and Methods 49:3, pages 726-737.
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Dário Ferreira, Sandra S. Ferreira, Célia Nunes, Miguel Fonseca & João T. Mexia. (2019) Chisquared and related inducing pivot variables: an application to orthogonal mixed models. Communications in Statistics - Theory and Methods 48:22, pages 5445-5466.
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Yalian Li & Hu Yang. (2016) More on the two-parameter estimation in the restricted regression. Communications in Statistics - Theory and Methods 45:24, pages 7184-7196.
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Siming Li, Yao Sheng & Yong Li. (2016) The quasi-stochastically constrained least squares method for ill linear regression. Communications in Statistics - Theory and Methods 45:2, pages 217-225.
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Jibo Wu & Hu Yang. (2015) On the Principal Component Liu-type Estimator in Linear Regression. Communications in Statistics - Simulation and Computation 44:8, pages 2061-2072.
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Articles from other publishers (3)

Dário Ferreira, Sandra S. Ferreira, Célia Nunes & João T. Mexia. (2020) Inference in nonorthogonal mixed models. Mathematical Methods in the Applied Sciences 45:5, pages 3183-3196.
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Yalian Li & Hu Yang. (2014) Efficiency of a stochastic restricted two-parameter estimator in linear regression. Applied Mathematics and Computation 249, pages 371-381.
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Daojiang He & Yan Wu. (2014) A Stochastic Restricted Principal Components Regression Estimator in the Linear Model. The Scientific World Journal 2014, pages 1-6.
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