A General Akaike-Type Criterion for Model Selection in Robust Regression
针对非最小二乘拟合（如最小绝对偏差回归）的模型选择问题，提出一种适用于多种凸损失函数的一般Akaike型准则，模拟显示其能良好近似预测误差。
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.