提出高效回归模型，更好检测非平稳信号中的异常点。
Efficient Regression Models for Scan Statistics
- 用核回归建模扫描统计，捕捉信号非平稳性
- 算法优化至线性时间，适用于长信号分析
- 在天文数据中成功识别平台化异常问题
我们提出一类新的回归模型，用于实值信号上的扫描统计分析。这些模型能更优拟合非平稳信号，以区分扫描统计识别出的区间异常。模型可表示广义似然比统计量。尽管原始方法对长度为n的信号需O(n⁴)计算量，我们通过算法改进，在最大区间宽度受限的假设下实现了线性时间算法。基于Nadaraya-Watson核回归的方法尤其有效，在合成异常检测和干涉天文学中真实存在的“平台化”问题识别中表现突出。
原文摘要 · Abstract (English)
We introduce a new class of regression models for scan statistics on real-valued signals. These allow for improved fitting of non-stationary signals to contrast with the interval anomalies identified by the scan statistics. Our models can represent generalized likelihood ratio statistics. While these methods naively require $O(n^4)$ for a length $n$ signal, we provide algorithmic improvements which lead to linear time algorithms (with assumptions on max interval width). Our methods, especially ones based on Nadaraya-Watson kernel regression, are demonstrated as especially effective in detecting both synthetically planted anomalies, and for identifying a real ``platforming'' issue in interferometric astronomy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。