An empirical comparison of information-theoretic selection criteria for multivariate behavior genetic models
An empirical comparison of information-theoretic selection criteria for multivariate behavior genetic models
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DOI：
10.1007/s10519-004-5587-0
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发表时间：
2004-11-01
影响因子：
2.6
通讯作者：
Krueger, RF
中科院分区：
文献类型：
--
作者：
Markon, KE;Krueger, RF
信息论为统计推断和模型选择提供了有吸引力的基础。然而，人们对不同信息理论标准在协方差结构建模中的相对性能知之甚少，特别是在行为遗传背景下。为了探讨这些问题，我们比较了信息论拟合标准在不同模型、分布和样本量条件下区分多变量行为遗传模型的能力。结果表明，性能取决于样本量、模型复杂性和分布规范。在一定条件下，贝叶斯信息准则（BIC）比赤池信息准则（AIC）具有更强的鲁棒性，并且在更大样本和比较更复杂模型时优于AIC。最小描述长度的近似(MDL; Rissanen， J.（1996）。李志强，李志强（2001）。IEEE信息理论汇刊47:1712-1717)标准，涉及经验费雪信息矩阵，由于估计费雪信息矩阵的复杂性，表现出可变的性能模式。结果表明，一种相对较新的信息理论标准，Draper的信息标准（DIC; Draper, 1995），它具有贝叶斯和MDL标准的特征，与BIC相似或优于BIC。结果强调了进一步研究信息论准则的理论和计算的重要性。
Information theory provides an attractive basis for statistical inference and model selection. However, little is known about the relative performance of different information-theoretic criteria in covariance structure modeling, especially in behavioral genetic contexts. To explore these issues, information-theoretic fit criteria were compared with regard to their ability to discriminate between multivariate behavioral genetic models under various model, distribution, and sample size conditions. Results indicate that performance depends on sample size, model complexity, and distributional specification. The Bayesian Information Criterion (BIC) is more robust to distributional misspecification than Akaike's Information Criterion (AIC) under certain conditions, and outperforms AIC in larger samples and when comparing more complex models. An approximation to the Minimum Description Length (MDL; Rissanen, J. (1996). IEEE Transactions on Information Theory 42: 40-47, Rissanen, J. (2001). IEEE Transactions on Information Theory 47: 1712-1717) criterion, involving the empirical Fisher information matrix, exhibits variable patterns of performance due to the complexity of estimating Fisher information matrices. Results indicate that a relatively new information-theoretic criterion, Draper's Information Criterion (DIC; Draper, 1995), which shares features of the Bayesian and MDL criteria, performs similarly to or better than BIC. Results emphasize the importance of further research into theory and computation of information-theoretic criteria.