基于混合建模的多维4PLM贝叶斯MH-RM算法

IF 1.5 3区心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-02 DOI:10.1111/bmsp.12300

Shaoyang Guo, Yanlei Chen, Chanjin Zheng, Guiyu Li

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引用次数: 0

摘要

最近的一些研究已经解决了一维四参数逻辑模型(4PLM)的估计问题。尽管做出了这些努力，这个问题仍然是多维4PLM (M4PLM)面临的一个挑战。Fu等人(2021)提出了一种用于M4PLM的Gibbs采样器，但它很耗时。本文提出了一种基于混合建模的贝叶斯MH-RM (MM-MH-RM)算法，用于M4PLM的最大后验估计。将MM-MH-RM算法与原始的MH-RM算法进行比较，两项仿真研究和一个实例表明，MM-MH-RM算法具有混合建模方法的优点，可以产生更稳健的估计，收敛速度有保证，计算速度快。MM-MH-RM算法的MATLAB代码可在在线附录中获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

Several 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.

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来源期刊

British Journal of Mathematical & Statistical Psychology 医学-数学跨学科应用

CiteScore

5.00

自引率

3.80%

发文量

审稿时长

>12 weeks

期刊介绍： The British Journal of Mathematical and Statistical Psychology publishes articles relating to areas of psychology which have a greater mathematical or statistical aspect of their argument than is usually acceptable to other journals including: • mathematical psychology • statistics • psychometrics • decision making • psychophysics • classification • relevant areas of mathematics, computing and computer software These include articles that address substantitive psychological issues or that develop and extend techniques useful to psychologists. New models for psychological processes, new approaches to existing data, critiques of existing models and improved algorithms for estimating the parameters of a model are examples of articles which may be favoured.