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Statistical agnostic regression: A machine learning method to validate regression models
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DOI：10.1016/j.jare.2025.04.026.png)
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摘要
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• A novel approach, SAR evaluates statistical significance in ML-based linear regression models by analyzing concentration inequalities of the expected loss (actual risk). • SAR introduces a threshold ensuring evidence of a linear relationship in the population, with a probability of at least 1-η1 - η1-η, under non-parametric assumptions. • Simulations show SAR can emulate the classical multivariate FFF-test for slope parameters, offering comparable analyses of variance without relying on traditional assumptions. • Residuals computed from SAR balance characteristics of ML-based and classical OLS residuals, bridging gaps between these methodologies.
Keyword:
Ordinary least squares
K-fold cross-validation
linear support vector machines
Statistical learning theory
Permutation tests
Upper bounding
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13
论文数:
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被引数:
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