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Admissible prediction in superpopulation models with random regression coefficients under matrix loss function

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  • Xu, Li-Wen
  • Yu, Sheng-Hua

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.

Suggested Citation

  • Xu, Li-Wen & Yu, Sheng-Hua, 2012. "Admissible prediction in superpopulation models with random regression coefficients under matrix loss function," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 68-76, January.
    • Handle: RePEc:eee:jmvana:v:103:y:2012:i:1:p:68-76
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    References listed on IDEAS

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    1. Liu, Xu-Qing & Rong, Jian-Ying, 2007. "Quadratic prediction problems in finite populations," Statistics & Probability Letters, Elsevier, vol. 77(5), pages 483-489, March.
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    Cited by:

    1. Yongge Tian, 2015. "A new derivation of BLUPs under random-effects model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 905-918, November.

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