FACTOR-ANALYSIS AND AIC
FACTOR-ANALYSIS AND AIC
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DOI：
10.1007/bf02294359
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发表时间：
1987-09-01
期刊：
影响因子：
3
通讯作者：
AKAIKE, H
中科院分区：
文献类型：
--
作者：
AKAIKE, H
引入信息准则AIC，将极大似然法推广到多模型情形。它是通过将自回归模型定阶的成功经验与最大似然因子分析中因子个数的确定联系起来得到的。当AIC标准被视为贝叶斯模型的选择时，它在因子分析中的使用尤其有趣。这一观察表明，AIC的应用领域可以比传统的I.I.D.广泛得多。标准的原始派生所基于的类型模型。通过观察因子分析模型的贝叶斯结构，我们可以通过引入因子负荷的自然先验分布来处理不适当解的问题。
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis. The use of the AIC criterion in the factor analysis is particularly interesting when it is viewed as the choice of a Bayesian model. This observation shows that the area of application of AIC can be much wider than the conventional i.i.d. type models on which the original derivation of the criterion was based. The observation of the Bayesian structure of the factor analysis model leads us to the handling of the problem of improper solution by introducing a natural prior distribution of factor loadings.