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On the modeling of the average value of high school national examination in West Java using Bayesian hierarchical mixture normal approach

机译：贝叶斯分层混合范式法在西爪哇高中全国考试平均数建模

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摘要

National Examination (UN) is one of the standard evaluation systems of education in Indonesia. The results of the UN can be used as a consideration for the development and provision of assistance to educational units in an effort to improve the quality of education. This research is done to get the best model of the average of UN value which is hierarchically structured. This paper would employ a two-level hierarchical linear model with nine characteristics of the school in the first level and four characteristics of the district/city in the second level. The complexity of the model is increasing due to the pattern of average UN value follows a normal mixture distribution pattern. A Bayesian hierarchical mixture normal approach coupled with Markov Chain Monte Carlo (MCMC), therefore, would be employed to estimate the model numerically. The results show that based on DIC value, the hierarchical normal mixture model with four components on the UN value has a better performance than the only one level mixture regression model in explaining the variability of the average value of UN.
机译：全国考试（UN）是印度尼西亚教育的标准评估系统之一。联合国的成果可作为发展和向教育部门提供援助的考虑因素，以努力提高教育质量。进行这项研究是为了获得最佳的联合国价值平均值模型，该模型具有层次结构。本文将采用两级分层线性模型，第一级具有九个学校的特征，第二级具有四个地区/城市的特征。由于平均UN值遵循正常的混合物分布模式，因此模型的复杂性正在增加。因此，将采用与马尔可夫链蒙特卡洛（MCMC）耦合的贝叶斯分层混合法线方法进行数值估计。结果表明，基于DIC值，UN值具有四个成分的分层正态混合模型在解释UN平均值的变异性方面比仅一级混合回归模型具有更好的性能。

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