IMPROVED ESTIMATION OF THE EXPECTED KULLBACK–LEIBLER DISCREPANCY IN CASE OF MISSPECIFICATION
研究了模型设定错误时AIC的偏差，给出了偏差的简单表达式以构建改进估计量，但发现基于改进估计量的模型选择与基于严重偏差估计量的结果几乎相同。
In case of misspecification, the Akaike information criterion (AIC; Akaike, 1973, in Petrov & Csaki, eds., Second International Symposium on Information Theory, pp. 267–281. Budapest: Akademia Kiado) is an asymptotically biased estimator of the expected Kullback–Leibler discrepancy. This paper gives simple expressions for the bias that can be used to construct improved estimators. However, for the examples that are considered in detail it turns out that model selection procedures based on such improved estimators are nearly equivalent to model selection procedures based on severely biased estimators.