Department of Business Analytics, Information Systems and Supply Chain.
Department of Psychological Sciences.
Psychol Methods. 2019 Dec;24(6):735-753. doi: 10.1037/met0000207. Epub 2019 Oct 7.
During the past 5 to 10 years, an estimation method known as has been used extensively to produce symptom networks (or, more precisely, symptom dependence graphs) from binary data in psychopathological research. The 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 ₁-regularized logistic regressions. In this article, we offer an unprecedented critique of the Ising model and . We provide a careful assessment of the conditions that underlie the Ising model as well as specific limitations associated with the estimation algorithm. This assessment leads to serious concerns regarding the implementation of 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 . Two psychopathological data sets are used to highlight the concerns that are raised in the critique. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
在过去的 5 到 10 年中，一种称为“最大熵模型”的估计方法已被广泛用于从精神病理学研究中的二元数据中生成症状网络（或者更准确地说，症状依赖图）。该方法基于一种特殊类型的伊辛模型，对应于二元成对马尔可夫随机场，其流行部分原因是基于一系列正则化逻辑回归的有效估计过程。在本文中，我们对伊辛模型和最大熵模型进行了前所未有的批判。我们对伊辛模型所依据的条件进行了仔细的评估，并对与最大熵估计算法相关的具体局限性进行了评估。这种评估引发了人们对最大熵在精神病理学研究中的实施的严重关注。消除或至少减轻这些关注的一些潜在策略包括：（a）使用分区或混合建模来解释受访者样本中未观察到的异质性，以及（b）使用症状相似性的共现度量来替代或补充与最大熵相关的协方差/相关度量。使用两个精神病理学数据集来突出本文批判中提出的关注点。（PsycINFO 数据库记录（c）2019 APA，保留所有权利）。
