Abstract
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.
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Maevskii, V.V., Kharin, Y.S. Robust Regressive Forecasting under Functional Distortions in a Model. Automation and Remote Control 63, 1803–1820 (2002). https://doi.org/10.1023/A:1020959432568
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DOI: https://doi.org/10.1023/A:1020959432568