Robust regressive forecasting under functional distortions in a model

V. V. Maevskii; Yu. S. Kharin

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Robust regressive forecasting under functional distortions in a model

机译：模型中功能失真下的鲁棒回归预测

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Regressive forecasting is investigated under the assumption that the hypothetical parametric model of the regression function admits functional distortions. Explicit expressions of prediction risk (mean-square error) for four main types of distortions, guaranteed risk, and robustness coefficient for the least-squares prediction algorithm are derived. The minimax risk criterion is used to construct a robust prediction algorithm from iteratively computed M-estimates of the parameters of the hypothetical regression function with a special loss function. Results of computer-aided experiments are given.

机译：在假设回归函数的参数模型允许功能失真的前提下，研究回归预测。推导了四种主要类型的失真，保证风险和最小二乘预测算法的鲁棒性系数的预测风险（均方误差）的明确表达式。最小最大风险标准用于根据具有特殊损失函数的假设回归函数的参数的迭代计算的M估计来构造鲁棒的预测算法。给出了计算机辅助实验的结果。

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《Automation and Remote Control》 |2002年第11期|共18页
作者
V. V. Maevskii; Yu. S. Kharin;
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1. Robust regressive forecasting under functional distortions in a model [J] . V. V. Maevskii, Yu. S. Kharin Automation and Remote Control . 2002,第11期

机译：模型中功能失真下的鲁棒回归预测
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3. Robust multivariate Bayesian forecasting under functional distortions in the chi(2)-metric [J] . Kharin AY, Shlyk PA Journal of Statistical Planning and Inference . 2009,第11期

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Robust regressive forecasting under functional distortions in a model

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