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- Publisher Website: 10.3389/fpsyg.2020.02260
- Scopus: eid_2-s2.0-85092320967
- PMID: 33101108
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Article: Estimating CDMs Using the Slice-Within-Gibbs Sampler
Title | Estimating CDMs Using the Slice-Within-Gibbs Sampler |
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Authors | |
Keywords | the slice-within-Gibbs sampler CDMs DINA model G-DINA model Gibbs sampling |
Issue Date | 2020 |
Publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/psychology |
Citation | Frontiers in Psychology, 2020, v. 11, p. article no. 2260 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/305477 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 0.800 |
PubMed Central ID | |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, X | - |
dc.contributor.author | de la Torre, J | - |
dc.contributor.author | Zhang, J | - |
dc.contributor.author | Guo, J | - |
dc.contributor.author | Shi, N | - |
dc.date.accessioned | 2021-10-20T10:09:56Z | - |
dc.date.available | 2021-10-20T10:09:56Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Frontiers in Psychology, 2020, v. 11, p. article no. 2260 | - |
dc.identifier.issn | 1664-1078 | - |
dc.identifier.uri | http://hdl.handle.net/10722/305477 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Frontiers Research Foundation. The Journal's web site is located at http://www.frontiersin.org/psychology | - |
dc.relation.ispartof | Frontiers in Psychology | - |
dc.rights | This Document is Protected by copyright and was first published by Frontiers. All rights reserved. It is reproduced with permission. | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | the slice-within-Gibbs sampler | - |
dc.subject | CDMs | - |
dc.subject | DINA model | - |
dc.subject | G-DINA model | - |
dc.subject | Gibbs sampling | - |
dc.title | Estimating CDMs Using the Slice-Within-Gibbs Sampler | - |
dc.type | Article | - |
dc.identifier.email | de la Torre, J: j.delatorre@hku.hk | - |
dc.identifier.authority | de la Torre, J=rp02159 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3389/fpsyg.2020.02260 | - |
dc.identifier.pmid | 33101108 | - |
dc.identifier.pmcid | PMC7545134 | - |
dc.identifier.scopus | eid_2-s2.0-85092320967 | - |
dc.identifier.hkuros | 328192 | - |
dc.identifier.volume | 11 | - |
dc.identifier.spage | article no. 2260 | - |
dc.identifier.epage | article no. 2260 | - |
dc.identifier.isi | WOS:000577855300001 | - |
dc.publisher.place | Switzerland | - |