A general Akaike-type criterion for model selection in robust regression
A general Akaike-type criterion for model selection in robust regression
复制标题
鲁棒回归中模型选择的一般赤池型准则
DOI：
10.1093/biomet/82.4.877
复制
发表时间：
1995
期刊：
影响因子：
2.7
通讯作者：
D. Nolan
中科院分区：
文献类型：
--
作者：
P. Burman;D. Nolan
赤池（Akaike, 1970）选择模型的程序使预测新的、独立的观测值时的预期平方误差估计最小化。该选择准则是为最小二乘拟合模型设计的。另一种不同的模型拟合技术，如最小绝对偏差回归，需要适当的模型选择过程。本文提出了一个通用的akaike型准则，适用于各种损失函数的模型拟合。它只要求函数是凸的，有唯一的极小值，并且在期望上可二阶微。仿真结果表明，本文提出的估计器能很好地逼近各自的预测误差。
Akaike's procedure (1970) for selecting a model minimises an estimate of the expected squared error in predicting new, independent observations. This selection criterion was designed for models fitted by least squares. A different model-fitting technique, such as least absolute deviation regression, requires an appropriate model selection procedure. This paper presents a general Akaike-type criterion applicable to a wide variety of loss functions for model fitting. It requires only that the function be convex with a unique minimum, and twice differentiable in expectation. Simulations show that the estimators proposed here well approximate their respective prediction errors.