On Ising Models and Algorithms for the Construction of Symptom Networks in Psychopathological Research
On Ising Models and Algorithms for the Construction of Symptom Networks in Psychopathological Research
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DOI：
10.1037/met0000207
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发表时间：
2019-12-01
影响因子：
7
通讯作者：
Wasserman, Stanley
中科院分区：
文献类型：
--
作者：
Brusco, Michael J.;Steinley, Douglas;Wasserman, Stanley
在过去的 5 到 10 年中，一种称为 eLasso 的估计方法已被广泛用于从精神病理学研究中的二进制数据生成症状网络（或更准确地说，症状依赖图）。 eLasso 方法基于特定类型的 Ising 模型，该模型对应于二元成对马尔可夫随机场，其受欢迎程度部分归因于基于一系列 l(1) 正则化逻辑回归的高效估计过程。在本文中，我们对伊辛模型和 eLasso 提出了前所未有的批评。我们对 Ising 模型的条件以及与 eLasso 估计算法相关的具体限制进行了仔细评估。这一评估引发了人们对 eLasso 在精神病理学研究中实施的严重担忧。消除或至少减轻这些担忧的一些潜在策略包括（a）使用分区或混合模型来解释受访者样本中未观察到的异质性，以及（b）使用共现测量来测量症状相似性，以取代或补充与 eLasso 相关的协方差/相关测量。两个精神病理学数据集用于强调批评中提出的担忧。
During the past 5 to 10 years, an estimation method known as eLasso has been used extensively to produce symptom networks (or, more precisely, symptom dependence graphs) from binary data in psychopathological research. The eLasso method is based on a particular type of Ising model that corresponds to binary pairwise Markov random fields, and its popularity is due, in part, to an efficient estimation process that is based on a series of l(1)-regularized logistic regressions. In this article, we offer an unprecedented critique of the Ising model and eLasso. We provide a careful assessment of the conditions that underlie the Ising model as well as specific limitations associated with the eLasso estimation algorithm. This assessment leads to serious concerns regarding the implementation of eLasso in psychopathological research. Some potential strategies for eliminating or, at least, mitigating these concerns include (a) the use of partitioning or mixture modeling to account for unobserved heterogeneity in the sample of respondents, and (b) the use of co-occurrence measures for symptom similarity to either replace or supplement the covariance/correlation measure associated with eLasso. Two psychopathological data sets are used to highlight the concerns that are raised in the critique.