Model selection information criteria for non-nested latent class models
Model selection information criteria for non-nested latent class models
复制标题
DOI：
10.3102/10769986022003249
复制
发表时间：
1997-09-01
影响因子：
2.4
通讯作者：
Dayton, CM
中科院分区：
文献类型：
--
作者：
Lin, TH;Dayton, CM
潜类模型已被开发用于评估分级和行为分析中的层次关系。本文研究了三种模型选择信息准则-- Akaike AIC、施瓦茨SIG和博兹多甘CAIC --在非嵌套模型中的使用。一般来说，SIC和CAIC上级AIC相对简单的模型，而AIC上级更复杂的模型，虽然准确性往往是相当低的Sor这样的模型。此外，在模型中的错误率检测到一些影响。
Latent class models have been developed for assessment of hierarchic hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria - Akaike AIC, Schwarz SIG, and Bozdogan CAIC - for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low Sor such models. In addition, some effects were detected for error rates in the models.