A Gibbs-INLA algorithm for multidimensional graded response model analysis

IF 1.5 3区心理学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-09-29 DOI:10.1111/bmsp.12321

Xiaofan Lin, Siliang Zhang, Yincai Tang, Xuan Li

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引用次数: 0

Abstract

In this paper, we propose a novel Gibbs-INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning which is inevitable in classical Markov chain Monte Carlo (MCMC) algorithm, and has low computing memory, high computational efficiency with much fewer iterations, and still achieve higher estimation accuracy. Therefore, it has the ability to handle large amount of multidimensional response data with different item responses. Simulation studies are conducted to compare with the Metroplis-Hastings Robbins-Monro (MH-RM) algorithm and an application to the study of the IPIP-NEO personality inventory data is given to assess the performance of the new algorithm. Extensions of the proposed algorithm for application on more complicated models and different data types are also discussed.

查看原文本刊更多论文

用于多维分级响应模型分析的Gibbs INLA算法。

在本文中，我们基于多维项目反应理论，提出了一种新的Gibbs INLA算法，用于具有顺序反应的分级反应模型的贝叶斯推理。通过将吉布斯采样和集成嵌套拉普拉斯近似（INLA）相结合，新框架避免了经典马尔可夫链蒙特卡罗（MCMC）算法中不可避免的繁琐调整，并且计算内存低，迭代次数少，计算效率高，并且仍然实现了更高的估计精度。因此，它能够处理具有不同项目响应的大量多维响应数据。将其与Metroplis-Hasttings-Robbins-Monro（MH-RM）算法进行了仿真研究，并将其应用于IPIP-NEO人格清单数据的研究，以评估新算法的性能。还讨论了所提出的算法在更复杂的模型和不同数据类型上的应用扩展。

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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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.