关于缺失数据的 Ising 网络分析的说明。

IF 2.9 2区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Psychometrika Pub Date : 2024-12-01 Epub Date: 2024-07-06 DOI:10.1007/s11336-024-09985-2
Siliang Zhang, Yunxiao Chen
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引用次数: 0

摘要

Ising 模型已成为分析项目反应数据的常用心理测量模型。伊辛模型的统计推断通常通过伪似然法进行,因为当变量(即项目)较多时,标准似然法的计算成本较高。遗憾的是,缺失值的存在会阻碍伪似然法的使用,而列表删除法处理缺失数据可能会给估计带来很大偏差,有时还会产生误导性解释。本文提出了一种用于缺失数据 Ising 网络分析的条件贝叶斯框架,该框架将伪似然法与迭代数据估算相结合。该方法建立了渐近理论。此外,还提出了一种计算高效的 Pólya-Gamma 数据扩增程序,以简化模型参数的采样。该方法的性能通过模拟和在真实世界中对全国酒精及相关疾病流行病学调查(NESARC)的重度抑郁症和广泛性焦虑症数据的应用得到了证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Note on Ising Network Analysis with Missing Data.

A Note on Ising Network Analysis with Missing Data.

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

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来源期刊
Psychometrika
Psychometrika 数学-数学跨学科应用
CiteScore
4.40
自引率
10.00%
发文量
72
审稿时长
>12 weeks
期刊介绍: The journal Psychometrika is devoted to the advancement of theory and methodology for behavioral data in psychology, education and the social and behavioral sciences generally. Its coverage is offered in two sections: Theory and Methods (T& M), and Application Reviews and Case Studies (ARCS). T&M articles present original research and reviews on the development of quantitative models, statistical methods, and mathematical techniques for evaluating data from psychology, the social and behavioral sciences and related fields. Application Reviews can be integrative, drawing together disparate methodologies for applications, or comparative and evaluative, discussing advantages and disadvantages of one or more methodologies in applications. Case Studies highlight methodology that deepens understanding of substantive phenomena through more informative data analysis, or more elegant data description.
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