Gibbs sampling using the data augmentation scheme for higher-order item response models

DOI10.1016/j.physa.2019.123696OpenAlexW2995176374WikidataQ126592788 ScholiaQ126592788MaRDI QIDQ2137645

Xue Zhang, Jian Tao, Zhihui Fu

Publication date: 16 May 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2019.123696

zbMATH Keywords

Bayesian estimationGibbs samplingdata augmentationHO-IRT model

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Uses Software

• WinBUGS
• Mplus
• BILOG

Cites Work

• A MCMC-method for models with continuous latent responses
• Item response theory for longitudinal data: population parameter estimation
• Multidimensional item reponse theory.
• Bayesian item response modeling. Theory and applications.
• On the relationship between the higher-order factor model and the hierarchical factor model
• Higher-order latent trait models for cognitive diagnosis
• Gibbs samplers for logistic item response models via the Pólya-gamma distribution: a computationally efficient data-augmentation strategy
• MCMC estimation and some model-fit analysis of multidimensional IRT models
• The Calculation of Posterior Distributions by Data Augmentation
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications

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