二元图解模型近似方法的经验比较研究;应用于法国死亡证明中死亡原因之间联系的研究
An empirical comparative study of approximate methods for binary graphical models; application to the search of associations among causes of death in French death certificates
摘要：
由于在生物学、医学和许多其他涉及统计应用的领域中遇到的数据量越来越多，寻找多个变量之间的关联是统计学中的一个热门问题。图形模型最近在统计文献中为此目的而流行。在为线性回归框架设计的LASSO过程的思想之后，最近处理图形模型选择的发展基于$\ell_1$ -惩罚。然而，在二进制情况下，由于所谓的对数配分函数的形式，精确推理通常非常缓慢甚至难以处理。最近在文献中提出了各种近似方法，本文的主要目的是比较它们。通过广泛的模拟研究，我们表明，一个简单的修改依赖于高斯近似的方法实现了良好的性能，是非常快的。我们提出了一个真正的应用程序中，我们寻找法国死亡证明上记录的死亡原因之间的关联。
英文摘要：
Looking for associations among multiple variables is a topical issue in statistics due to the increasing amount of data encountered in biology, medicine and many other domains involving statistical applications. Graphical models have recently gained popularity for this purpose in the statistical literature. Following the ideas of the LASSO procedure designed for the linear regression framework, recent developments dealing with graphical model selection have been based on $\ell_1$-penalization. In the binary case, however, exact inference is generally very slow or even intractable because of the form of the so-called log-partition function. Various approximate methods have recently been proposed in the literature and the main objective of this paper is to compare them. Through an extensive simulation study, we show that a simple modification of a method relying on a Gaussian approximation achieves good performance and is very fast. We present a real application in which we search for associations among causes of death recorded on French death certificates.
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