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arXiv · 1309.0220

Least Product Relative Error Estimation

Kani Chen·Yuanyuan Lin·Zhanfeng Wang·Zhiliang Ying
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Abstract

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 plugging-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.

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Kani Chen, Yuanyuan Lin, Zhanfeng Wang, Zhiliang Ying. 2013-09-01. Least Product Relative Error Estimation. https://arxiv.org/abs/1309.0220

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