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Multivariate calibration with robust signal regression

机译:具有鲁棒信号回归的多变量校准

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摘要

Motivated by a multivariate calibration problem from a soil characterization study, we proposed tractable and robust variants of penalized signal regression (PSR) using a class of non-convex Huber-like criteria as the loss function. Standard methods may fail to produce a reliable estimator, especially when there are heavy-tailed errors. We present a computationally efficient algorithm to solve this non-convex problem. Simulation and empirical examples are extremely promising and show that the proposed algorithm substantially improves the PSR performance under heavy-tailed errors.
机译:通过土壤特征研究的多变量校准问题,我们使用一类非凸页状标准作为损耗功能提出了惩罚信号回归(PSR)的贸易和鲁棒变体。 标准方法可能无法产生可靠的估计器,尤其是当存在重尾误差时。 我们提出了一种计算有效的算法来解决这个非凸面问题。 仿真和经验示例非常有前途,并且表明所提出的算法在重尾误差下大大提高了PSR性能。

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