提出一种基于散度的模型加权平均方法，提升小样本下预测准确性。
A Divergence-Based Method for Weighting and Averaging Model Predictions
- 用最小散度框架计算模型权重，适用于各类建模方法。
- 小样本下性能优于或媲美传统加权平均与堆叠方法。
- 理论证明其在小样本中更稳健，适合数据有限场景。
本文基于最小散度框架，提出一种新的模型加权方法，用于对统计与机器学习模型的概率预测进行平均。该方法具有通用性，适用于通过频率学派、贝叶斯或其他方式拟合的模型。方法从两种不同角度出发，实证结果表明，在样本量较小时，其表现优于或等同于标准模型平均方法，包括模型堆叠及基于AIC风格负指数加权的平均法。理论分析揭示了该方法在小样本条件下具备优势的原因。
原文摘要 · Abstract (English)
This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictions from statistical and machine learning models. The method is general and can be applied regardless of whether the models under consideration are fit to data using frequentist, Bayesian, or some other fitting method. The proposed method is motivated in two different ways and is shown empirically to perform better than or on a par with standard model averaging methods, including model stacking and model averaging that relies on Akaike-style negative exponentiated model weighting, especially when the sample size is small. Our theoretical analysis explains why the method has a small-sample advantage.
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