报告主题：Change-Point Detection with Local Trend Adjustment

时间：2025年10月10日 13:00-13:45

地点：腾讯会议 会议号：350-8579-6257

报告人：贾圣吉

报告内容简介：

        Identifying the number and precise locations of multiple change points in long sequences is a critical issue in statistics and machine learning. However, accurate change point detection can be compromised by the presence of local trends in the sequence when using the conventional parametric piecewise-constant model. In this paper, we introduce an adaptive Neyman test to assess the presence of local trends. Subsequently, we develop a novel changepoint detection procedure based on a partially linear model that incorporates these local trends. Furthermore, we extend the proposed testing and estimation methods to multidimensional cases, facilitating the identification of common change points in array-based data. Our methods are straightforward to implement, and we evaluate their numerical performance through simulations and the analysis of SNP genotyping data.

主讲人简介：