$\Phi$ admissibility of stochastic regression coefficients in a general multivariate random effects model under a generalized balanced loss function

Ming-Xiang Cao

doi:10.1214/12-ejs747

$\Phi$ admissibility of stochastic regression coefficients in a general multivariate random effects model under a generalized balanced loss function

Electronic Journal of Statistics ◽

10.1214/12-ejs747 ◽

2012 ◽

Vol 6 (0) ◽

pp. 2332-2346 ◽

Cited By ~ 3

Author(s):

Ming-Xiang Cao

Keyword(s):

Random Effects ◽

Loss Function ◽

Regression Coefficients ◽

Random Effects Model ◽

Balanced Loss Function ◽

Stochastic Regression

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Estimation of regression coefficients subject to exact linear restrictions when some observations are missing and quadratic error balanced loss function is used

Test ◽

10.1007/bf02595409 ◽

2005 ◽

Vol 14 (2) ◽

pp. 385-396 ◽

Cited By ~ 10

Author(s):

H. Toutenburg ◽

Shalabh

Keyword(s):

Loss Function ◽

Regression Coefficients ◽

Quadratic Error ◽

Balanced Loss Function ◽

Linear Restrictions

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Linearly admissible estimators of stochastic regression coefficient under balanced loss function

Statistics ◽

10.1080/02331888.2013.766794 ◽

2013 ◽

Vol 48 (2) ◽

pp. 359-366 ◽

Cited By ~ 3

Author(s):

Mingxiang Cao ◽

Xingzhong Xu ◽

Daojiang He

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Stochastic Regression

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Bayesian Estimation of Regression Coefficients Under Extended Balanced Loss Function

Communication in Statistics- Theory and Methods ◽

10.1080/03610926.2012.725498 ◽

2014 ◽

Vol 43 (20) ◽

pp. 4253-4264 ◽

Cited By ~ 6

Author(s):

Anoop Chaturvedi ◽

Shalabh

Keyword(s):

Bayesian Estimation ◽

Loss Function ◽

Regression Coefficients ◽

Balanced Loss Function

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The linear minimax estimator of stochastic regression coefficients and parameters under quadratic loss function

Statistics & Probability Letters ◽

10.1016/j.spl.2006.05.011 ◽

2007 ◽

Vol 77 (1) ◽

pp. 54-62 ◽

Cited By ~ 6

Author(s):

Sheng-Hua Yu

Keyword(s):

Loss Function ◽

Regression Coefficients ◽

Quadratic Loss ◽

Minimax Estimator ◽

Quadratic Loss Function ◽

Stochastic Regression

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AN ESTIMATION PROCEDURE FOR ERROR VARIANCE INCORPORATING PTS FOR RANDOM EFFECTS MODEL UNDER LINEX LOSS FUNCTION

Communication in Statistics- Theory and Methods ◽

10.1081/sta-100108449 ◽

2001 ◽

Vol 30 (12) ◽

pp. 2583-2599 ◽

Cited By ~ 4

Author(s):

Rakesh Srivastava ◽

Vilpa Tanna

Keyword(s):

Random Effects ◽

Loss Function ◽

Estimation Procedure ◽

Error Variance ◽

Random Effects Model ◽

Linex Loss Function ◽

Linex Loss

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admissibility for linear estimators on regression coefficients in a general multivariate linear model under balanced loss function

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2009.03.013 ◽

2009 ◽

Vol 139 (9) ◽

pp. 3354-3360 ◽

Cited By ~ 16

Author(s):

Mingxiang Cao

Keyword(s):

Linear Model ◽

Loss Function ◽

Regression Coefficients ◽

Multivariate Linear Model ◽

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 ◽

Cited By ~ 2

Author(s):

Mingxiang Cao

Keyword(s):

Loss Function ◽

Regression Coefficient ◽

Balanced Loss Function ◽

Linear Estimators ◽

Stochastic Regression

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Constrained Bayes and empirical Bayes estimation under random effects normal ANOVA model with balanced loss function

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2007.08.004 ◽

2008 ◽

Vol 138 (7) ◽

pp. 2017-2028 ◽

Cited By ~ 14

Author(s):

Malay Ghosh ◽

Myung Joon Kim ◽

Dal Ho Kim

Keyword(s):

Random Effects ◽

Loss Function ◽

Empirical Bayes ◽

Bayes Estimation ◽

Empirical Bayes Estimation ◽

Balanced Loss Function ◽

Anova Model ◽

Constrained Bayes

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199. The Random Effects Model Applied to RCF Workplace Monitoring Data

10.3320/1.2763037 ◽

1999 ◽

Author(s):

L. Maxim ◽

J. Allshouse ◽

D. Venturin

Keyword(s):

Random Effects ◽

Monitoring Data ◽

Random Effects Model ◽

Workplace Monitoring

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Measurement Invariance Testing Using Random Effects model for Many groups: Multilevel Confirmatory Factor Analysis (ML CFA) and Multilevel Factor Mixture Modeling (ML FMM)

THE KOREAN JOURNAL OF PSYCHOLOGY GENERAL ◽

10.22257/kjp.2019.6.38.2.185 ◽

2019 ◽

Vol 38 (2) ◽

pp. 185-218

Author(s):

Sookyung Son ◽

Hyojin Kim ◽

Sehee Hong

Keyword(s):

Factor Analysis ◽

Confirmatory Factor Analysis ◽

Measurement Invariance ◽

Random Effects ◽

Mixture Modeling ◽

Random Effects Model ◽

Invariance Testing ◽

Multilevel Confirmatory Factor Analysis ◽

Factor Mixture Modeling ◽

Confirmatory Factor

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