Adaptive Testing of Conditional Association Through Recursive Mixture Modeling
通过递归混合建模进行条件关联的自适应检验AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦病例对照数据的条件关联检验，未明确建立因果模型或离散选择行为模型，分类依据不足。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
通过递归混合建模进行条件关联的自适应检验
- 研究问题
- 病例对照研究中，协变量空间高维且传统分层过于稀疏时，如何自适应寻找有效分层并检验预测变量与响应的条件关联？
- 方法与思路
- 作者在预测变量的回顾性分布上建立递归混合模型，以协变量空间分割的先验描述潜在分层，并通过递推完成贝叶斯推断。
- 材料陈述的贡献
- 摘要称，该框架可通过一系列递推进行高效闭式推断，在模型灵活性与计算可行性之间取得平衡，模拟显示优于经典检验。
- 阅读关注点
- 应核对分割先验和递归规则、条件关联检验的决策方式，以及分层学习如何影响误报控制、功效与计算复杂度。
- 阅读边界
- 摘要未说明检验校准条件、先验敏感性或比较数值，条件关联不等于因果效应。所据索引摘要版本未注明，刊本变化未知。
原文摘要
In many case-control studies, a central goal is to test for association or dependence between the predictors and the response. Relevant covariates must be conditioned on to avoid false positives and loss in power. Conditioning on covariates is easy in parametric frameworks such as the logistic regression—by incorporating the covariates into the model as additional variables. In contrast, nonparametric methods such as the Cochran-Mantel-Haenszel test accomplish conditioning by dividing the data into strata, one for each possible covariate value. In modern applications, this often gives rise to numerous strata, most of which are sparse due to the multidimensionality of the covariate and/or predictor space, while in reality, the covariate space often consists of just a small number of subsets with differential response-predictor dependence. We introduce a Bayesian approach to inferring from the data such an effective stratification and testing for association accordingly. The core of our framework is a recursive mixture model on the retrospective distribution of the predictors, whose mixing distribution is a prior on the partitions on the covariate space. Inference under the model can proceed efficiently in closed form through a sequence of recursions, striking a balance between model flexibility and computational tractability. Simulation studies show that our method substantially outperforms classical tests under various scenarios. Supplementary materials for this article are available online.