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A Bayesian algorithm based on auxiliary variables for estimating GRM with non-ignorable missing data

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Abstract

In this paper, a highly effective Bayesian sampling algorithm based on auxiliary variables is used to estimate the graded response model with non-ignorable missing response data. Compared with the traditional marginal likelihood method and other Bayesian algorithms, the advantages of the new algorithm are discussed in detail. Based on the Markov Chain Monte Carlo samples from the posterior distributions, the deviance information criterion and the logarithm of the pseudomarignal likelihood are employed to compare the different missing mechanism models. Two simulation studies are conducted and a detailed analysis of the sexual compulsivity scale data is carried out to further illustrate the proposed methodology.

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Authors and Affiliations

  1. Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, School of Mathematics and Statistics, Yunnan University, 2 North Cuihu Road, Wuhua District, 650091, Kunming, Yunnan, China

    Jiwei Zhang

  2. School of Mathematics and Statistics, Yili Normal University, 448 Jie Fang Road, 835000, Yili, Xinjiang, China

    Zhaoyuan Zhang

  3. Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, 5268 Ren Min Street, Nanguan District, 130024, Changchun, Jilin, China

    Jian Tao

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  1. Jiwei Zhang
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  2. Zhaoyuan Zhang
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  3. Jian Tao
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Correspondence to Zhaoyuan Zhang.

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Zhang, J., Zhang, Z. & Tao, J. A Bayesian algorithm based on auxiliary variables for estimating GRM with non-ignorable missing data. Comput Stat 36, 2643–2669 (2021). https://doi.org/10.1007/s00180-021-01100-8

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