Admissible prediction in superpopulation models with random regression coefficients under matrix loss function |
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Authors: | Li-Wen Xu Sheng-Hua Yu |
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Affiliation: | a College of Sciences, North China University of Technology, Beijing 100144, PR Chinab College of Economics and Trade, Hunan University, Changsha 410079, PR China |
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Abstract: | Admissible prediction problems in finite populations with arbitrary rank under matrix loss function are investigated. For the general random effects linear model, we obtained the necessary and sufficient conditions for a linear predictor of the linearly predictable variable to be admissible in the two classes of homogeneous linear predictors and all linear predictors and the class that contains all predictors, respectively. Moreover, we prove that the best linear unbiased predictors (BLUPs) of the population total and the finite population regression coefficient are admissible under different assumptions of superpopulation models respectively. |
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Keywords: | 62M20 62D05 62C15 15A09 |
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