PLACE:  319 Snedecor

SPEAKER:
WeiBiao Wu
Department of Statistics, University of Michigan

TITLE:
Change-point Problem

ABSTRACT:

Many time series can be modeled as the sum of three components:  long-time
trend, seasonal effect and background noise. The trend superimposed with the
seasonal effect constitute the mean of the process. The issue of mean
stationarity is usually the first step for further statistical inference. In
this talk, we present a theory of testing and estimation for a monotonic trend
and the identification of seasonal effects. Testing is cast as a generic
change-point problem, or probabilistic diagnostics.  The change-point problem
has been one of the central issues of statistical inference for several
decades. It includes, for example, testing for changes in weather patterns and
disease rates. We are mainly concerned with a posteriori testing, using
spectral
analysis to determine periodic components and isotonic regression to estimate
the trend. A distinctive feature of our approach is that these two problems can
be treated
simultaneously: isotonic regression gives estimators for long-time trend with
negligible influence from seasonal effects.
 

COFFEE: 3:45 p.m., 104 Snedecor