Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data.
Bayesian Inference for Growth Mixture Models with Latent Class Dependent Missing Data.
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DOI：
10.1080/00273171.2011.589261
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发表时间：
2011-07-01
影响因子：
3.8
通讯作者：
Lubke G
中科院分区：
文献类型：
--
作者：
Lu ZL;Zhang Z;Lubke G
具有不可重复缺失数据的混合增长模型（Growth mixture models，GMM）在研究界引起了越来越多的关注，但尚未得到充分的研究。本文的目的是提出和评估贝叶斯方法来估计潜在类相关缺失数据的Gestival。首先提出了一种扩展的GMM，其中类概率依赖于一些观察到的解释变量和数据丢失依赖于解释变量和潜在的类变量。一个完整的贝叶斯方法，然后提出了估计模型。通过数据增广方法，得到了所有模型参数和缺失数据的条件后验分布。吉布斯抽样程序，然后用于生成马尔可夫链的模型参数的统计推断。通过对1997年全国青少年数学能力成长纵向调查数据的分析，首次展示了该模型和方法的应用。考虑3个主要因素（样本量，类概率，和缺失数据机制）的模拟研究，然后进行，结果表明，所提出的贝叶斯估计方法进行了很好的研究条件下。最后，本研究的一些影响，包括误指定的缺失机制，样本量，模型的灵敏度，潜在类的数量，模型比较，以及未来的方向的方法，进行了讨论。
Growth mixture models (GMMs) with nonignorable missing data have drawn increasing attention in research communities but have not been fully studied. The goal of this article is to propose and to evaluate a Bayesian method to estimate the GMMs with latent class dependent missing data. An extended GMM is first presented in which class probabilities depend on some observed explanatory variables and data missingness depends on both the explanatory variables and a latent class variable. A full Bayesian method is then proposed to estimate the model. Through the data augmentation method, conditional posterior distributions for all model parameters and missing data are obtained. A Gibbs sampling procedure is then used to generate Markov chains of model parameters for statistical inference. The application of the model and the method is first demonstrated through the analysis of mathematical ability growth data from the National Longitudinal Survey of Youth 1997. A simulation study considering 3 main factors (the sample size, the class probability, and the missing data mechanism) is then conducted and the results show that the proposed Bayesian estimation approach performs very well under the studied conditions. Finally, some implications of this study, including the misspecified missingness mechanism, the sample size, the sensitivity of the model, the number of latent classes, the model comparison, and the future directions of the approach, are discussed.
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DOI：
10.1111/1467-9868.00236
发表时间：
2000-01-01
影响因子：
5.8
作者：
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通讯作者：
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DOI：
10.1348/000711009x475187
发表时间：
2010-11-01
影响因子：
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作者：
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作者：
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