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

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  • Guikai Hu
  • Qingguo Li
  • Shenghua Yu

Abstract

Under a balanced loss function, we investigate the minimax prediction of finite population regression coefficient in a superpopulation model with Gauss–Markov type errors. The linear minimax predictor (LMP) proved to be admissible in the class of homogeneous linear predictors is obtained. Under the balanced loss function, we further prove that LMP dominates the best linear unbiased predictor (BLUP) proposed by Bolfarine et al. [Bolfarine et al., Optimal prediction of the finite population regression coefficient. Sankhya‾$\bar{\rm a}$. Ser. B. 56 (1994) 1–10] on certain conditions. Moreover, a numerical example is performed to illustrate the theoretical results.

Suggested Citation

  • Guikai Hu & Qingguo Li & Shenghua Yu, 2016. "Linear minimax prediction of finite population regression coefficient under a balanced loss function," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(24), pages 7197-7209, December.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:24:p:7197-7209
    DOI: 10.1080/03610926.2014.978945
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