دورية أكاديمية
أعرض في EDS

The impact of ordinal scales on Gaussian mixture recovery.

التفاصيل البيبلوغرافية
العنوان: The impact of ordinal scales on Gaussian mixture recovery.
المؤلفون: Haslbeck JMB; Psychological Methods Group, University of Amsterdam, Amsterdam, Netherlands. jonashaslbeck@gmail.com., Vermunt JK; Department of Methodology and Statistics, Tilburg University, Tilburg, Netherlands., Waldorp LJ; Psychological Methods Group, University of Amsterdam, Amsterdam, Netherlands.
المصدر: Behavior research methods [Behav Res Methods] 2023 Jun; Vol. 55 (4), pp. 2143-2156. Date of Electronic Publication: 2022 Jul 13.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Country of Publication: United States NLM ID: 101244316 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1554-3528 (Electronic) Linking ISSN: 1554351X NLM ISO Abbreviation: Behav Res Methods Subsets: MEDLINE
أسماء مطبوعة: Publication: 2010- : New York : Springer
Original Publication: Austin, Tex. : Psychonomic Society, c2005-
مواضيع طبية MeSH: Algorithms*, Humans ; Bayes Theorem ; Normal Distribution
مستخلص: Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation-maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable.
(© 2022. The Author(s).)
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فهرسة مساهمة: Keywords: Gaussian Mixture Modeling; Misspecification; Mixture modeling; Model selection; Ordinal scales
تواريخ الأحداث: Date Created: 20220713 Date Completed: 20230612 Latest Revision: 20240430
رمز التحديث: 20240501
مُعرف محوري في PubMed: PMC10250525
DOI: 10.3758/s13428-022-01883-8
PMID: 35831565
قاعدة البيانات: MEDLINE
الوصف
تدمد:1554-3528
DOI:10.3758/s13428-022-01883-8