Corrected Mallows criterion for model averaging
Corrected Mallows criterion for model averaging
复制标题
DOI：
10.1016/j.csda.2019.106902
复制
发表时间：
2020-04
期刊：
影响因子：
--
通讯作者：
Jun Liao;Guohua Zou
中科院分区：
文献类型：
--
作者：
Jun Liao;Guohua Zou
模型平均方法的一个重要问题是权重的选择。汉森（2007）提出的Mallow准则是第一个渐近最优准则，得到了广泛的应用。在本文中，作者提出了一种基于F分布的小样本修正的Malows模型平均（MMAc）方法。MMAc在最小化平方误差的意义上表现出与Malows模型平均（MMA）相同的渐近最优性。基于MMAc的权重趋于最优权重最小化MSE的一致性也进行了研究。作者推导出新的经验权重的收敛速度。汉森和拉辛（2012）的MMA和刀切模型平均（JMA）也建立了类似的性质。一个广泛的模拟研究表明，MMAc往往比MMA和其他常用的模型平均方法，特别是对于小样本和中等样本量的情况下，更好地执行。真实的数据分析结果也支持了该方法。
An important problem with model averaging approach is the choice of weights. The Mallows criterion for choosing weights suggested by Hansen (2007) is the first asymptotically optimal criterion, which has been used widely. In the current paper, the authors propose a corrected Mallows model averaging (MMAc) method based on F distribution in small sample sizes. MMAc exhibits the same asymptotic optimality as Mallows model averaging (MMA) in the sense of minimizing the squared errors. The consistency of the MMAc based weights tending to the optimal weights minimizing MSE is also studied. The authors derive the convergence rate of the new empirical weights. Similar property for MMA and Jackknife model averaging (JMA) by Hansen and Racine (2012) is established as well. An extensive simulation study shows that MMAc often performs better than MMA and other commonly used model averaging methods, especially for small and moderate sample size cases. The results from the real data analysis also support the proposed method.