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

Autor: Laixu Shang, Ping-Feng Xu, Na Shan, Man-Lai Tang, George To-Sum Ho

Publikováno v: PLoS ONE, Vol 18, Iss 1, p e0279918 (2023)

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-

Externí odkaz: https://doaj.org/article/887c50f6137745558650cf1795e59a9b

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Latent variable sdelection in multidimensional item response theory models using the expectation model selection algorithm

Autor: Ping‐Feng Xu, Laixu Shang, Qian‐Zhen Zheng, Na Shan, Man‐Lai Tang

Publikováno v: The British journal of mathematical and statistical psychologyReferences. 75(2)

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39e69c4e8170d9f7373bfbd22fe51d12
https://pubmed.ncbi.nlm.nih.gov/34918834

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