Prof. Dr. David Siegmund (Stanford University)

Abstract:

We consider the problem of segmentation of (usually normal) observations according to changes in their mean. Changes can occur continuously, e.g., a change in the slope of a regression line, or discontinuously, e.g., a jump in the level of a process. Theoretical results will be illustrated by applications to copy number changes, historical weather records, and COVID-19--daily incidence, wastewater analysis, and excess deaths. Sequential detection, confidence regions for the change-points, and difficulties associated with dependent observations will also be discussed.

Aspects of this research involve collaboration with Fang Xiao, Li Jian, Liu Yi, Nancy Zhang, Benjamin Yakir, Keith Worsley, and Li (Charlie) Xia.