PERFORMANCE OF INFORMATION CRITERIA FOR MODEL SELECTION IN A LATENT GROWTH CURVE MIXTURE MODEL
PERFORMANCE OF INFORMATION CRITERIA FOR MODEL SELECTION IN A LATENT GROWTH CURVE MIXTURE MODEL
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DOI：
10.5183/jjscs.1309001_207
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
S. Usami
中科院分区：
文献类型：
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作者：
S. Usami
在考虑真实模型复杂性和模型错配影响的情况下，研究了基于似然和基于熵的信息准则在估计潜在生长曲线混合模型中类别数量方面的性能。仿真结果表明：(1)模型复杂度的增加会使所有准则的性能恶化，这在贝叶斯信息准则（BIC）和一致赤池信息准则（CAIC）中表现得尤为明显。(2)分类似然信息准则（CLC）和综合完全似然准则与BIC近似（ICL.BIC）经常低估类的数量。(3)基于熵的准则更频繁地正确估计类的数量。(4)当正态混合不正确地拟合包括异常值在内的非正态数据时，尽管这严重恶化了许多标准的性能，如BIC、CAIC和ICL。BIC相对稳健。此外，当样本量很大时，可以检测到混合比例非常小的过度提取类。(5)当存在测量的上界时，尽管这会使几乎所有准则的性能恶化，但基于熵的准则是稳健的。虽然没有单一的标准总是最好的，但ICL。BIC的平均表现更好。
Novel simulation studies are performed to investigate the performance of likelihood-based and entropy-based information criteria for estimating the number of classes in latent growth curve mixture models, considering influences of true model complexity and model misspecification. Simulation results can be summarized as (1) Increased model complexity worsens the performance of all criteria, and this is salient in Bayesian Information Criteria (BIC) and Consistent Akaike Information Criteria (CAIC). (2) The classification likelihood information criterion (CLC) and integrated completed likelihood criterion with BIC approximation (ICL.BIC) frequently underestimate the number of classes. (3) Entropy-based criteria correctly estimate the number of classes more frequently. (4) When a normal mixture is incorrectly fit to non-normal data including outliers, although this seriously worsens the performance of many criteria, BIC, CAIC, and ICL.BIC are relatively robust. Additionally, overextracted classes with trivially small mixture proportions can be detected when the sample size is large. (5) When there is an upper bound of measurement, although this worsens the performance of almost all criteria, entropy-based criteria are robust. (6) Although no single criterion is always best, ICL.BIC shows better performance on average.