NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION
NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION
复制标题
DOI：
10.1109/tac.1974.1100705
复制
发表时间：
1974-01-01
影响因子：
6.8
通讯作者：
AKAIKE, H
中科院分区：
文献类型：
--
作者：
AKAIKE, H
本文简要回顾了时间序列分析中统计假设检验的发展历史，指出假设检验过程不能被充分地定义为统计模型识别的过程。回顾了经典的极大似然估计方法，介绍了一种新的用于统计辨识的最小信息理论准则（AIC）估计（MAICE）。当存在多个竞争模型时，MAICE由模型和参数的最大似然估计值定义，该最大似然估计值给出由AIC =（-2）log-（最大似然）+ 2（模型内独立调整的参数数量）定义的AIC最小值。MAICE提供了一个通用的统计模型识别程序，它是从传统的假设检验程序的应用程序中固有的模糊性。最后通过数值算例说明了MAICE在时间序列分析中的实用性。
The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.