Dynamic statistical inference in massive datastreams
海量数据流中的动态统计推断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。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
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：摘要明确分析多条持续增长的时间序列，并用变系数模型进行在线跟踪和筛查，支持时间序列方法分类。
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…
海量数据流中的动态统计推断
- 研究问题
- 面对多条持续增长的时间序列，如何在线更新动态模型参数，并快速识别行为偏离多数序列的个体及异常发生时段？
- 方法与思路
- 作者提出动态跟踪与筛查框架，在变系数模型中进行稳健递推估计，允许不等间隔设计，并结合数据驱动平滑和流式多重检验。
- 材料陈述的贡献
- 摘要称方法无需保存历史数据即可更新估计，并提供统计保证、模拟评估和移动健康应用，用于定位活动与睡眠对情绪的异常影响时段。
- 阅读关注点
- 应核对异常序列和多数基准的定义、在线平滑参数选择、多重检验的误差控制，以及递推更新在时间变化下的保证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实。摘要未列出统计条件及检测数值，健康应用中的“影响”也不能直接视为因果效应。
原文摘要
Modern technological advances have expanded the scope of applications requiring analysis of large-scale datastreams that comprise multiple indefinitely long time series. There is an acute need for statistical methodologies that perform online inference and continuously revise the model to reflect the current status of the underlying process. In this manuscript, we propose a dynamic statistical inference framework--named dynamic tracking and screening (DTS)--that is not only able to provide accurate estimates of the underlying parameters in a dynamic statistical model, but also capable of rapidly identifying irregular individual streams whose behavioral patterns deviate from the majority. Concretely, by fully exploiting the sequential feature of datastreams, we develop a robust estimation approach under a framework of varying coefficient model. The procedure naturally accommodates unequally-spaced design points and updates the coefficient estimates as new data arrive without the need to store historical data. A data-driven choice of an optimal smoothing parameter is accordingly proposed. Furthermore, we suggest a new multiple testing procedure tailored to the streaming environment. The resulting DTS scheme is able to adapt time-varying structures appropriately, track changes in the underlying models, and hence maintain high accuracy in detecting time periods during which individual streams exhibit irregular behavior. Moreover, we derive rigorous statistical guarantees of the procedure and investigate its finite-sample performance through simulation studies. We demonstrate the proposed methods through a mobile health example to estimate the timings when subjects' sleep and physical activities have unusual influence upon their mood.