The Bayesian estimators of polytomous item response theory models with approximated conditional likelihood and their mathematical optimalities,
The Bayesian estimators of polytomous item response theory models with approximated conditional likelihood and their mathematical optimalities,
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具有近似条件似然性的多分项响应理论模型的贝叶斯估计量及其数学最优性，
DOI：
10.1109/bigdata.2016.7841046
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
T.
中科院分区：
文献类型：
--
作者：
Mori;K. and Ohmori;T.
在这项研究中，我们得到了具有近似条件似然函数的多分类项目反应理论(IRT)模型的贝叶斯估计，并考虑了它们的数学最优性。首先，我们利用文献[10]提出的条件型近似似然函数，给出了评级尺度模型(RSM)及其相关IRT模型的贝叶斯估计。然后，我们对这些估计量的可容许性和极小性进行了估计。然后，我们通过模拟研究，给出了条件似然方法的逼近范围。文中还给出了应用实例。
In this study, we derive the Bayesian estimators of polytomous item response theory (IRT) models with an approximated conditional likelihood function and consider their mathematical optimalities. First, we derive Bayesian estimators of the rating scale model (RSM) and related IRT models with a conditional type approximated likelihood function proposed by [10]. Then, we evaluate the admissibility and minimaxity of these estimators. We then show the range of the approximation by the conditional likelihood method using a simulation study. An application is also presented.