Prof. David Siegmund

I will discuss a general framework for detection of local signals, primarily defined by change-points, in sequences of data. 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. I will focus on the problem of segmentation of independent normal observations according to changes in the mean. Results will be illustrated by simulations and applications to copy number changes and to historical weather patterns. Confidence regions for the change- points and some 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 and Li (Charlie) Xia.