ESTIMATING HETEROGENEOUS GRAPHICAL MODELS FOR DISCRETE DATA WITH AN APPLICATION TO ROLL CALL VOTING.
ESTIMATING HETEROGENEOUS GRAPHICAL MODELS FOR DISCRETE DATA WITH AN APPLICATION TO ROLL CALL VOTING.
复制标题
估计具有拨打呼叫投票的应用程序的离散数据的异质图形模型。
DOI：
10.1214/13-aoas700
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Zhu J
中科院分区：
文献类型：
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作者：
Guo J;Cheng J;Levina E;Michailidis G;Zhu J
我们考虑的问题，联合估计一组离散数据的图形模型，对应于几个类别，共享一些共同的结构。这种设置的一个例子是立法者在不同问题上的投票记录，如国防，能源和医疗保健。我们开发了一个马尔可夫图形模型来描述这些数据所产生的异构依赖结构。该模型通过联合估计方法进行拟合，该方法保留了底层的公共图结构，但也允许网络之间的差异。该方法采用了一个群体惩罚，目标是所有网络中的共同零交互作用效应。我们应用该方法来描述美国参议院的内部网络上的几个重要问题。我们的分析揭示了每个问题的个别结构，与我们能够单独提取的所有类别所共有的基本的众所周知的两党结构不同。我们还建立了所提出的方法的一致性参数估计和模型选择，并评估其数值性能上的一些模拟的例子。
We consider the problem of jointly estimating a collection of graphical models for discrete data, corresponding to several categories that share some common structure. An example for such a setting is voting records of legislators on different issues, such as defense, energy, and healthcare. We develop a Markov graphical model to characterize the heterogeneous dependence structures arising from such data. The model is fitted via a joint estimation method that preserves the underlying common graph structure, but also allows for differences between the networks. The method employs a group penalty that targets the common zero interaction effects across all the networks. We apply the method to describe the internal networks of the U.S. Senate on several important issues. Our analysis reveals individual structure for each issue, distinct from the underlying well-known bipartisan structure common to all categories which we are able to extract separately. We also establish consistency of the proposed method both for parameter estimation and model selection, and evaluate its numerical performance on a number of simulated examples.