Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).
Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC).
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DOI：
10.1037/a0027127
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发表时间：
2012-06
影响因子：
7
通讯作者：
Vrieze, Scott I.
中科院分区：
文献类型：
--
作者：
Vrieze, Scott I.
关键词：
本文综述了赤池信息准则（AIC）和贝叶斯信息准则（BIC）在模型选择和心理学理论评价中的应用。重点是潜在变量模型，因为它们在理论测试和构建中的使用越来越多。我们讨论了回归的理论统计结果，并说明了更重要的问题与新的模拟涉及潜在变量模型，包括因子分析，潜在的配置文件分析，因子混合模型。渐进地，BIC是一致的，因为它将选择真实模型，如果在其他假设中，真实模型在考虑的候选模型中。AIC在这些情况下并不一致。当真实模型不在候选模型集中时，AIC是有效的，因为它将渐进地选择使预测/估计的均方误差最小化的模型。BIC在这些情况下是无效的。与BIC不同，AIC也具有极大极小属性，因为它可以在有限的样本大小下最大限度地降低可能的风险。总之，AIC和BIC具有非常不同的性质，需要不同的假设，应用研究人员和方法学家都将受益于对这些准则的渐近和有限样本行为的更好理解。使用AIC或BIC的最终决定取决于许多因素，包括：所采用的损失函数，研究的方法设计，实质性研究问题，以及真正模型的概念及其对手头研究的适用性。
This article reviews the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on latent variable models given their growing use in theory testing and construction. We discuss theoretical statistical results in regression and illustrate more important issues with novel simulations involving latent variable models including factor analysis, latent profile analysis, and factor mixture models. Asymptotically, the BIC is consistent, in that it will select the true model if, among other assumptions, the true model is among the candidate models considered. The AIC is not consistent under these circumstances. When the true model is not in the candidate model set the AIC is effcient, in that it will asymptotically choose whichever model minimizes the mean squared error of prediction/estimation. The BIC is not effcient under these circumstances. Unlike the BIC, the AIC also has a minimax property, in that it can minimize the maximum possible risk in finite sample sizes. In sum, the AIC and BIC have quite different properties that require different assumptions, and applied researchers and methodologists alike will benefit from improved understanding of the asymptotic and finite-sample behavior of these criteria. The ultimate decision to use AIC or BIC depends on many factors, including: the loss function employed, the study's methodological design, the substantive research question, and the notion of a true model and its applicability to the study at hand.
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DOI：
10.1016/s0378-3758(02)00336-1
发表时间：
2003-03-01
影响因子：
0.9
作者：
Berger, JO;Ghosh, JK;Mukhopadhyay, N
通讯作者：
Mukhopadhyay, N
影响因子：
3
作者：
BOZDOGAN, H
通讯作者：
BOZDOGAN, H
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
4.5
作者：
FOSTER, DP;GEORGE, EI
通讯作者：
GEORGE, EI
DOI：
10.1080/10705510709336744
发表时间：
2007-01-01
影响因子：
6
作者：
Henson, James M.;Reise, Steven P.;Kim, Kevin H.
通讯作者：
Kim, Kevin H.