论文标题
基于经验可能性的非钉模型选择
Nonnested model selection based on empirical likelihood
论文作者
论文摘要
我们建议对非参数模型选择进行经验可能性比测试，其中竞争模型可以嵌套，未扣，重叠，误导或正确指定。它比较了基于交叉验证的模型的平方预测误差，并允许模型误差的异质性。我们开发其渐近分布，用于比较加性模型和不同的模型，并将其扩展到具有大量数据的加成模型中变量的显着性。该方法适用于监督学习模型之间的模型选择。为了促进测试的实施，我们提供了快速计算过程。模拟表明，所提出的测试效果很好，并且与某些现有方法相比具有有限的样本性能。该方法在经验应用上进行了验证。
We propose an empirical likelihood ratio test for nonparametric model selection, where the competing models may be nested, nonnested, overlapping, misspecified, or correctly specified. It compares the squared prediction errors of models based on the cross-validation and allows for heteroscedasticity of the errors of models. We develop its asymptotic distributions for comparing additive models and varying-coefficient models and extend it to test significance of variables in additive models with massive data. The method is applicable to model selection among supervised learning models. To facilitate implementation of the test, we provide a fast calculation procedure. Simulations show that the proposed tests work well and have favorable finite sample performance over some existing approaches. The methodology is validated on an empirical application.
