Admissibility for linear estimators of regression coefficient in a general Gauss–Markoff model under balanced loss function

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2010.05.004 ◽

2010 ◽

Vol 140 (11) ◽

pp. 3365-3375 ◽

Author(s):

Guikai Hu ◽

Ping Peng

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Linear Estimators

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All admissible linear estimators of a regression coefficient under a balanced loss function

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2011.04.003 ◽

2011 ◽

Vol 102 (8) ◽

pp. 1217-1224 ◽

Author(s):

Guikai Hu ◽

Ping Peng

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Linear Estimators

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Admissible linear estimators of multivariate regression coefficient with respect to an inequality constraint under matrix balanced loss function

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2014.04.005 ◽

2014 ◽

Vol 129 ◽

pp. 37-43 ◽

Author(s):

Daojiang He ◽

Jie Wu

Keyword(s):

Loss Function ◽

Multivariate Regression ◽

Regression Coefficient ◽

Inequality Constraint ◽

Balanced Loss Function ◽

Linear Estimators

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Admissibility of linear estimators for the stochastic regression coefficient in a general Gauss–Markoff model under a balanced loss function

Journal of Multivariate Analysis ◽

10.1016/j.jmva.2013.10.015 ◽

2014 ◽

Vol 124 ◽

pp. 25-30 ◽

Author(s):

Mingxiang Cao

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Linear Estimators ◽

Stochastic Regression

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Linearly admissible estimators on linear functions of regression coefficient under balanced loss function

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2018.1472788 ◽

2019 ◽

Vol 48 (11) ◽

pp. 2700-2706

Author(s):

MingXiang Cao ◽

DaoJiang He

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Linear Functions ◽

Balanced Loss Function

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Minimax estimator of regression coefficient in normal distribution under balanced loss function

Linear Algebra and its Applications ◽

10.1016/j.laa.2011.08.013 ◽

2012 ◽

Vol 436 (5) ◽

pp. 1228-1237 ◽

Author(s):

Guikai Hu ◽

Qingguo Li ◽

Ping Peng

Keyword(s):

Normal Distribution ◽

Loss Function ◽

Regression Coefficient ◽

Minimax Estimator ◽

Balanced Loss Function

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Linearly admissible estimators of stochastic regression coefficient under balanced loss function

10.1080/02331888.2013.766794 ◽

2013 ◽

Vol 48 (2) ◽

pp. 359-366 ◽

Author(s):

Mingxiang Cao ◽

Xingzhong Xu ◽

Daojiang He

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Stochastic Regression

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Characterization of admissible linear estimators under extended balanced loss function

Kybernetika ◽

10.14736/kyb-2021-4-0613 ◽

2021 ◽

pp. 613-627

Author(s):

Buatikan Mirezi ◽

Selahattin Kaçıranlar

Keyword(s):

Loss Function ◽

Balanced Loss Function ◽

Linear Estimators

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Φ admissibility of linear estimators of common mean parameter in general multivariate linear models under a balanced loss function

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2019.1710757 ◽

2020 ◽

pp. 1-16

Author(s):

Mingxiang Cao ◽

Junyong Park ◽

Guangjun Shen

Keyword(s):

Loss Function ◽

Linear Models ◽

Common Mean ◽

Balanced Loss Function ◽

Linear Estimators ◽

Multivariate Linear Models

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General admissibility for linear estimators in a general multivariate linear model under balanced loss function

Acta Mathematica Sinica English Series ◽

10.1007/s10114-013-9246-3 ◽

2013 ◽

Vol 29 (9) ◽

pp. 1823-1832 ◽

Author(s):

Ming Xiang Cao ◽

Fan Chao Kong

Keyword(s):

Linear Model ◽

Loss Function ◽

Multivariate Linear Model ◽

Balanced Loss Function ◽

Linear Estimators

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Linear minimax prediction of finite population regression coefficient under a balanced loss function

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2014.978945 ◽

2016 ◽

Vol 45 (24) ◽

pp. 7197-7209

Author(s):

Guikai Hu ◽

Qingguo Li ◽

Shenghua Yu

Keyword(s):

Loss Function ◽

Finite Population ◽

Regression Coefficient ◽

Balanced Loss Function

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