MODEL SELECTION AND AKAIKE INFORMATION CRITERION (AIC) - THE GENERAL-THEORY AND ITS ANALYTICAL EXTENSIONS
MODEL SELECTION AND AKAIKE INFORMATION CRITERION (AIC) - THE GENERAL-THEORY AND ITS ANALYTICAL EXTENSIONS
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DOI：
10.1007/bf02294361
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发表时间：
1987-09-01
期刊：
影响因子：
3
通讯作者：
BOZDOGAN, H
中科院分区：
文献类型：
--
作者：
BOZDOGAN, H
在过去的15年中，赤池的基于熵的信息准则（AIC）对统计模型评价问题产生了根本性的影响。本文研究了AIC过程的一般理论，并在不违背赤池基本原理的前提下，从两方面对其进行了解析扩展。这些扩展使AIC渐近一致，并更严格地惩罚过度参数化，以只选择最简单的“真实”模型。这些选择标准被称为caaic和CAICF。研究了AIC及其扩展的渐近性质，并在两个不同条件下的蒙特卡罗实验中研究了这些准则在选择多项式模型的正确程度方面的经验性能。
During the last fifteen years, Akaike's entropy-based Information Criterion (AIC) has had a fundamental impact in statistical model evaluation problems. This paper studies the general theory of the AIC procedure and provides its analytical extensions in two ways without violating Akaike's main principles. These extensions make AIC asymptotically consistent and penalize overparameterization more stringently to pick only the simplest of the “true” models. These selection criteria are called CAIC and CAICF. Asymptotic properties of AIC and its extensions are investigated, and empirical performances of these criteria are studied in choosing the correct degree of a polynomial model in two different Monte Carlo experiments under different conditions.