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Bayesian latent variable model for mixed continuous and ordinal responses with possibility of missing responses

机译:连续和有序反应混合的贝叶斯潜变量模型,可能会丢失反应

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摘要

A general framework is proposed for joint modelling of mixed correlated ordinal and continuous responses with missing values for responses, where the missing mechanism for both kinds of responses is also considered. Considering the posterior distribution of unknowns given all available information, a Markov Chain Monte Carlo sampling algorithm via winBUGS is used for estimating the posterior distribution of the parameters. For sensitivity analysis to investigate the perturbation from missing at random to not missing at random, it is shown how one can use some elements of covariance structure. These elements associate responses and their missing mechanisms. Influence of small perturbation of these elements on posterior displacement and posterior estimates is also studied. The model is illustrated using data from a foreign language achievement study.
机译:提出了一个通用框架,用于混合建模的相关序数和连续响应的联合模型,其中缺少响应值,其中还考虑了两种响应的缺失机制。考虑到给定所有可用信息的未知数的后验分布,通过WinBUGS的马尔可夫链蒙特卡洛采样算法用于估计参数的后验分布。为了进行敏感性分析以研究从随机缺失到随机缺失的扰动,它显示了如何使用协方差结构的某些元素。这些要素将响应及其缺失的机制联系在一起。还研究了这些要素的小扰动对后位移和后估计的影响。使用来自外语成绩研究的数据说明了该模型。

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