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Accelerating L1-penalized expectation maximization algorithm for latent variable selection in multidimensional two-parameter logistic models.

التفاصيل البيبلوغرافية
العنوان: Accelerating L1-penalized expectation maximization algorithm for latent variable selection in multidimensional two-parameter logistic models.
المؤلفون: Laixu Shang, Ping-Feng Xu, Na Shan, Man-Lai Tang, George To-Sum Ho
المصدر: PLoS ONE, Vol 18, Iss 1, p e0279918 (2023)
بيانات النشر: Public Library of Science (PLoS), 2023.
سنة النشر: 2023
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: One of the main concerns in multidimensional item response theory (MIRT) is to detect the relationship between observed items and latent traits, which is typically addressed by the exploratory analysis and factor rotation techniques. Recently, an EM-based L1-penalized log-likelihood method (EML1) is proposed as a vital alternative to factor rotation. Based on the observed test response data, EML1 can yield a sparse and interpretable estimate of the loading matrix. However, EML1 suffers from high computational burden. In this paper, we consider the coordinate descent algorithm to optimize a new weighted log-likelihood, and consequently propose an improved EML1 (IEML1) which is more than 30 times faster than EML1. The performance of IEML1 is evaluated through simulation studies and an application on a real data set related to the Eysenck Personality Questionnaire is used to demonstrate our methodologies.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1932-6203
Relation: https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0279918
URL الوصول: https://doaj.org/article/887c50f6137745558650cf1795e59a9b
رقم الأكسشن: edsdoj.887c50f6137745558650cf1795e59a9b
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19326203
DOI:10.1371/journal.pone.0279918