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Least product relative error estimation

Authors Chen, Kani View this author's profile
Lin, Yuanyuan
Wang, Zhanfeng
Ying, Zhiliang
Issue Date 2016
Source Journal of Multivariate Analysis , v. 144, February 2016, p. 91-98
Summary A least product relative error criterion is proposed for multiplicative regression models. It is invariant under scale transformation of the outcome and covariates. In addition, the objective function is smooth and convex, resulting in a simple and uniquely defined estimator of the regression parameter. It is shown that the estimator is asymptotically normal and that the simple plug-in variance estimation is valid. Simulation results confirm that the proposed method performs well. An application to body fat calculation is presented to illustrate the new method. © 2015 Elsevier Inc.
ISSN 0047-259X
Language English
Format Article
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