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

Guikai Hu, Qingguo Li and Shenghua Yu

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 24, 7197-7209

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.

Date: 2016
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DOI: 10.1080/03610926.2014.978945

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