Estimating CDMs Using the Slice-Within-Gibbs Sampler

Frontiers in Psychology

article

Xin Xu¹  Jimmy de la Torre²  Jiwei Zhang³  Jinxin Guo¹  Ningzhong Shi¹ 

[1] Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University;Faculty of Education, The University of Hong Kong;Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University

关键词: the slice-within-Gibbs sampler;    CDMs;    DINA model;    G-DINA model;    Gibbs sampling;    MH algorithm;   

DOI  :  10.3389/fpsyg.2020.02260

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

In this paper, the slice-within-Gibbs sampler has been introduced as a method for estimating cognitive diagnosis models (CDMs). Compared with other Bayesian methods, the slice-within-Gibbs sampler can employ a wide-range of prior specifications; moreover, it can also be applied to complex CDMs with the aid of auxiliary variables, especially when applying different identifiability constraints. To evaluate its performances, two simulation studies were conducted. The first study confirmed the viability of the slice-within-Gibbs sampler in estimating CDMs, mainly including G-DINA and DINA models. The second study compared the slice-within-Gibbs sampler with other commonly used Markov Chain Monte Carlo algorithms, and the results showed that the slice-within-Gibbs sampler converged much faster than the Metropolis-Hastings algorithm and more flexible than the Gibbs sampling in choosing the distributions of priors. Finally, a fraction subtraction dataset was analyzed to illustrate the use of the slice-within-Gibbs sampler in the context of CDMs.

【 授权许可】

CC BY