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Estimating CDMs Using the Slice-Within-Gibbs Sampler.
Front Psychol. 2020; 11:2260.FP

Abstract

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.

Authors+Show Affiliations

Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.Faculty of Education, The University of Hong Kong, Pokfulam, Hong Kong.Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, Kunming, China.Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

33101108

Citation

Xu, Xin, et al. "Estimating CDMs Using the Slice-Within-Gibbs Sampler." Frontiers in Psychology, vol. 11, 2020, p. 2260.
Xu X, de la Torre J, Zhang J, et al. Estimating CDMs Using the Slice-Within-Gibbs Sampler. Front Psychol. 2020;11:2260.
Xu, X., de la Torre, J., Zhang, J., Guo, J., & Shi, N. (2020). Estimating CDMs Using the Slice-Within-Gibbs Sampler. Frontiers in Psychology, 11, 2260. https://doi.org/10.3389/fpsyg.2020.02260
Xu X, et al. Estimating CDMs Using the Slice-Within-Gibbs Sampler. Front Psychol. 2020;11:2260. PubMed PMID: 33101108.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Estimating CDMs Using the Slice-Within-Gibbs Sampler. AU - Xu,Xin, AU - de la Torre,Jimmy, AU - Zhang,Jiwei, AU - Guo,Jinxin, AU - Shi,Ningzhong, Y1 - 2020/09/25/ PY - 2020/06/02/received PY - 2020/08/11/accepted PY - 2020/10/26/entrez PY - 2020/10/27/pubmed PY - 2020/10/27/medline KW - CDMs KW - DINA model KW - G-DINA model KW - Gibbs sampling KW - MH algorithm KW - the slice-within-Gibbs sampler SP - 2260 EP - 2260 JF - Frontiers in psychology JO - Front Psychol VL - 11 N2 - 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. SN - 1664-1078 UR - https://www.unboundmedicine.com/medline/citation/33101108/Estimating_CDMs_Using_the_Slice_Within_Gibbs_Sampler_ L2 - https://doi.org/10.3389/fpsyg.2020.02260 DB - PRIME DP - Unbound Medicine ER -
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