Error Bound of Mode-Based Additive Models
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DOI: 10.3390/e23060651
Abstract: Due to their flexibility and interpretability, additive models are powerful tools for high-dimensional mean regression and variable selection. However, the least-squares loss-based mean regression models suffer from sensitivity to non-Gaussian noises, and there is also… read more here.
Keywords: additive models; regression; bound mode; error bound ... See more keywords