<scp>Mixture‐modelling‐based</scp> Bayesian <scp>MH‐RM</scp> algorithm for the multidimensional <scp>4PLM</scp>-Reference-Cited by-同舟云学术

Mixture‐modelling‐based Bayesian MH‐RM algorithm for the multidimensional 4PLM

Published:2023-02-02 Issue:3 Volume:76 Page:585-604
ISSN:0007-1102
Container-title:British Journal of Mathematical and Statistical Psychology
language:en
Short-container-title:Brit J Math & Statis

Author:

Guo Shaoyang¹^ORCID,Chen Yanlei²,Zheng Chanjin¹^ORCID,Li Guiyu³

Affiliation:

1. Shanghai Institute of AI for Education and Department of Educational Psychology of Faculty of Education East China Normal University Shanghai China

2. School of Educational Science Liaocheng University Liaocheng China

3. Faculty of Education, Institute of Curriculum & Instruction East China Normal University Shanghai China

Abstract

AbstractSeveral recent works have tackled the estimation issue for the unidimensional four‐parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time‐consuming. In this paper, a mixture‐modelling‐based Bayesian MH‐RM (MM‐MH‐RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM‐MH‐RM algorithm to the original MH‐RM algorithm, two simulation studies and an empirical example demonstrated that the MM‐MH‐RM algorithm possessed the benefits of the mixture‐modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM‐MH‐RM algorithm are available in the online appendix.

Publisher

Wiley

Subject

General Psychology,Arts and Humanities (miscellaneous),General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/bmsp.12300

Reference48 articles.

1. MCMC estimation and some model-fit analysis of multidimensional IRT models

2. Cai L.(2008).A Metropolis‐Hastings Robbins‐Monro algorithm for maximum likelihood nonlinear latent structure analysis with a comprehensive measurement model. [doctoral dissertation University of North Carolina at Chapel Hill]. Chapel Hill.