Bayesian criterion based model assessment for categorical data
分类数据的贝叶斯准则式模型评估AI 解读 ↓
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分类数据的贝叶斯准则式模型评估
- 研究问题
- 如何利用后验预测分布评估分类数据模型，并通过与未来响应变异相关的权重协调预测损失和模型复杂度？
- 方法与思路
- 作者提出加权 L 度量，以未来响应向量的抽样方差确定权重，研究理论性质并设计校准、模拟和双变量有序响应应用。
- 材料陈述的贡献
- 摘要称权重具有复杂度惩罚作用，加权准则在若干设置下优于未加权版本，并提出正式比较候选模型的校准方法。
- 阅读关注点
- 应核对权重与模型维度惩罚的关系、先验和损失选择、校准的比较含义，以及优势出现于哪些具体设置。
- 阅读边界
- OpenAlex 摘要版本未注明；未提供性能数值或完整适用条件，分类响应本身也不足以说明这是经济离散选择模型。
原文摘要
We propose a general Bayesian criterion for model assessment for categorical data called the weighted L measure, which is constructed from the posterior predictive distribution of the data. The measure is based on weighting the observations according to the sampling variance of their future response vector. The weight component in the weighted L measure plays the role of a penalty term in the criterion, in which a greater weight assigned to covariate values implies a greater penalty term on the dimension of the model. A detailed justification is provided for such a weighting procedure and several theoretical properties of the weighted L measure are presented for a wide variety of discrete data models. For these models, we examine properties of the weighted L measure, and show that it can perform better than the unweighted L measure in a variety of settings. In addition, we show that the weighted quadratic loss L measure is more attractive than the unweighted L measure and the deviance loss L measure for categorical data. Moreover, a calibration for the weighted L measure is motivated and proposed, which allows us to compare formally the L measure values of competing models. A detailed simulation study is presented to examine the performance of the weighted L measure, and it is compared to other established model‐selection methods. Finally, the method is applied to a real dataset using a bivariate ordinal response model.