用改进的Morgan-Pitman检验评估模型预测误差方差，提升选型可靠性。
The Morgan-Pitman Test of Equality of Variances and its Application to Machine Learning Model Evaluation and Selection
- 基于经典Morgan-Pitman法，增强对重尾分布和异常值的鲁棒性。
- 通过独立化残差策略，确保检验在机器学习场景下的有效性。
- 适用于需严格比较模型稳定性的科研与工业场景。
非线性模型的选型常侧重性能指标而忽略统计检验，难以考虑采样变异性。本文提出使用统计检验来评估预测误差的方差相等性。该方法基于经典的Morgan-Pitman方法，引入改进以增强对重尾分布或高方差异常值的鲁棒性，并设计了使机器学习模型残差统计独立的策略。通过一系列模拟实验和真实数据应用，验证了该检验在多种情境下的有效性和实用性，为模型评估与选择提供了一个可靠工具。
原文摘要 · Abstract (English)
Model selection in non-linear models often prioritizes performance metrics over statistical tests, limiting the ability to account for sampling variability. We propose the use of a statistical test to assess the equality of variances in forecasting errors. The test builds upon the classic Morgan-Pitman approach, incorporating enhancements to ensure robustness against data with heavy-tailed distributions or outliers with high variance, plus a strategy to make residuals from machine learning models statistically independent. Through a series of simulations and real-world data applications, we demonstrate the test's effectiveness and practical utility, offering a reliable tool for model evaluation and selection in diverse contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。