文章基本信息

标题：Estimating CDMs Using the Slice-Within-Gibbs Sampler
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作者：Xu, Xin ; de la Torre, Jimmy ; Zhang, Jiwei 等
期刊名称：Frontiers in Psychology
电子版ISSN：1664-1078
出版年度：2020
卷号：11
页码：1-15
DOI：10.3389/fpsyg.2020.02260
出版社：Frontiers Media
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.
关键词：the slice-within-Gibbs sampler; CDMS; DINA model; G-DINA model; gibbs sampling; MH algorithm