DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA
DESCRIBING DISABILITY THROUGH INDIVIDUAL-LEVEL MIXTURE MODELS FOR MULTIVARIATE BINARY DATA
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DOI：
10.1214/07-aoas126
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发表时间：
2007-12-01
影响因子：
1.8
通讯作者：
Joutard, Cyrille
中科院分区：
文献类型：
--
作者：
Erosheva, Elena A.;Fienberg, Stephen E.;Joutard, Cyrille
在美国，功能性残疾的数据引起了广泛的政策兴趣，特别是在为日益增多的老年人制定医疗保险和社会保障计划方面。我们认为提取的功能性残疾的数据前面的国家长期护理调查（NLTCS），并试图开发残疾概况使用的会员等级（GoM）模型的变化。我们首先将GoM描述为一个个体级混合模型，该模型允许个体同时在几个混合组分中具有部分成员资格。然后，我们证明了个体水平和人口水平的混合模型之间的等价性，并利用这一性质，开发一个马尔可夫链蒙特卡罗算法的贝叶斯估计的模型。我们使用我们的方法来分析NLTCS的功能残疾数据。
Data on functional disability are of widespread policy interest in the United States, especially with respect to planning for Medicare and Social security for a growing population of elderly adults. We consider an extract of functional disability data front the National Long Term Care Survey (NLTCS) and attempt to develop disability profiles using variations of the Grade of Membership (GoM) model. We first describe GoM as an individual-level mixture model that allows individuals to have partial membership in several mixture components simultaneously. We then prove the equivalence between individual-level and population-level mixture models, and use this property to develop a Markov Chain Monte Carlo algorithm for Bayesian estimation of the model. We Use our approach to analyze functional disability data from the NLTCS.