Change-point detection in multinomial data with a large number of categories
大量类别多项数据的变点检测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：摘要直接研究序列中类别概率的突变及多个变点估计，对应结构变化方法。
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…
大量类别多项数据的变点检测
- 研究问题
- 当多项数据的类别数接近或超过样本规模，且类别概率在未知位置突变时，如何检测并估计多个变点？
- 方法与思路
- 作者提出允许类别数趋于无穷的检测统计量，用分段惩罚目标选择变点数量，并通过动态规划估计变点位置。
- 材料陈述的贡献
- 摘要称，统计量原假设分布渐近正态，多变点估计在一定条件下一致；模拟与真实数据分析展示检测和定位表现。
- 阅读关注点
- 核对类别概率与维数增长条件、最小变化幅度和间隔、惩罚项选择，以及变点数与位置一致性的具体定义。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；具体条件、定位速率及有限样本数值未给出，不能断言任意稀疏类别配置下均可靠。
原文摘要
We consider a sequence of multinomial data for which the probabilities associated with the categories are subject to abrupt changes of unknown magnitudes at unknown locations. When the number of categories is comparable to or even larger than the number of subjects allocated to these categories, conventional methods such as the classical Pearson’s chi-squared test and the deviance test may not work well. Motivated by high-dimensional homogeneity tests, we propose a novel change-point detection procedure that allows the number of categories to tend to infinity. The null distribution of our test statistic is asymptotically normal and the test performs well with finite samples. The number of change-points is determined by minimizing a penalized objective function based on segmentation, and the locations of the change-points are estimated by minimizing the objective function with the dynamic programming algorithm. Under some mild conditions, the consistency of the estimators of multiple change-points is established. Simulation studies show that the proposed method performs satisfactorily for identifying change-points in terms of power and estimation accuracy, and it is illustrated with an analysis of a real data set.