Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.
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DOI：
10.1534/g3.115.021154
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发表时间：
2015-08-18
期刊：
影响因子：
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通讯作者：
Eskridge K
中科院分区：
文献类型：
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作者：
Montesinos-López OA;Montesinos-López A;Crossa J;Burgueño J;Eskridge K
迄今为止开发的大多数基因组预测模型都假设响应变量是连续的且正态分布的。例外的是概率单位模型，为有序分类表型开发。在统计应用中，由于贝叶斯概率位有序回归（BPOR）模型易于实现，贝叶斯逻辑有序回归（BLOR）很少在基因组预测的背景下实现[样本量（n）远小于参数数量（p）]。出于这个原因，在本文中，我们提出了一个使用Pólya-Gamma数据增强方法的BLOR模型，该方法产生了一个具有BPOR模型类似的全条件分布的Gibbs采样器，并且具有BPOR模型是BLOR模型的特殊情况的优点。我们通过模拟和两个真实的数据集来评估所提出的模型。结果表明，我们的BLOR模型是一个很好的替代分析顺序数据的背景下，基因组启用的预测与概率或logit链接。
Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link.