Slice sampling algorithm for estimating the item response theory model with ordinal response data

被引：1

作者：

Zhang, Jiwei ^{[1
]}

Zhang, Zhaoyuan ^{[2
]}

Lu, Jing ^{[3
]}

机构：

[1] Yunnan Univ, Sch Math & Stat, Kunming, Yunnan, Peoples R China

[2] Yili Normal Univ, Sch Math & Stat, Yili, Peoples R China

[3] Northeast Normal Univ, Sch Math & Stat, Key Lab Appl Stat MOE, Changchun 130024, Jilin, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2023年 / 52卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Bayesian estimation; Item response theory; Gibbs sampling algorithm; Graded response model; Metropolis-Hastings sampling algorithm; Slice sampling algorithm; CHAIN MONTE-CARLO; BAYESIAN-ESTIMATION; LATENT ABILITY; MISSING DATA; IRT; PARAMETERS; DISTRIBUTIONS; CONVERGENCE; SEEKING; MCMC;

D O I：

10.1080/03610918.2021.1974477

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper, a highly effective slice sampling algorithm is proposed to estimate the graded response model that has been widely used in educational psychological assessments. The new algorithm not only avoids the Metroplis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability, but also overcomes the dependence of the traditional Gibbs sampling algorithm on the conjugate prior distribution. Three simulation studies are conducted and a detailed analysis of sexual compulsivity scale (SCS) data is carried out to further illustrate the proposed methodology.

引用

页码：4909 / 4932

页数：24

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