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.