Semiparametric Bayesian classification with longitudinal markers
Semiparametric Bayesian classification with longitudinal markers
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DOI：
10.1111/j.1467-9876.2007.00569.x
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发表时间：
2007-01-01
影响因子：
1.6
通讯作者：
Mueller, Peter
中科院分区：
文献类型：
--
作者：
De la Cruz-Mesia, Rolando;Quintana, Fernando A.;Mueller, Peter
我们分析了一项涉及173名孕妇的研究数据。这些数据是在孕龄的前80天内测量的β人绒毛膜促性腺激素的观察值，包括每个妇女的1至6个纵向反应。本研究的主要目的是从妊娠早期的数据中预测正常妊娠结局与异常妊娠结局。我们实现了所需的分类与半参数层次模型。具体来说，我们考虑一个狄利克雷过程的混合物在每个组中的随机效应的分布之前。允许未知随机效应分布在各组之间变化，但通过使用设计向量来选择单个潜在随机概率度量的不同特征，使其具有依赖性。由此产生的模型是一个扩展的依赖狄利克雷过程模型，与一个额外的概率模型组分类。该模型被证明比替代模型，这是基于独立的Dirichlet过程的组执行更好。利用马尔可夫链蒙特卡罗方法总结了相关的后验分布。
We analyse data from a study involving 173 pregnant women. The data are observed values of the beta human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.