Bayesian Multiple Breakpoint Detection: Mixing Documented and Undocumented Changepoints

Robert Lund, Clemson University, Mathematical Sciences

Friday, September 14, 2018 - 3:30pm

This talk presents methods to estimate the number of changepoint time(s) and their locations in time-ordered data sequences when prior information is known about some of the changepoint times.  A Bayesian version of a penalized likelihood objective function is developed from minimum description length (MDL) information theory principles.  Optimizing the objective function yields estimates of the changepoint number(s) and location time(s).  Our MDL penalty depends on where the
changepoint(s) lie, but not solely on the total number of changepoints (such as classical AIC and BIC penalties). Specifically, configurations with changepoints that occur relatively closely to one and other are penalized more heavily than sparsely arranged changepoints.  The techniques allow for autocorrelation in the observations and mean shifts at each changepoint time.  This scenario arises in climate time series where a ``metadata" record exists documenting some, but not necessarily
all, of station move times and instrumentation changes. Applications to climate time series are presented throughout.

Seminars generally take place in 116 Old Chemistry Building on Fridays from 3:30 - 4:30 pm. For additional information contact: karen.whitesell@duke.edu or phone 919-684-8029. Sorry, but we do not have reprints available. Please feel free to contact the authors by email for follow-up information, articles, etc. Reception following seminar in 211 Old Chemistry

Old Chemistry 116

Location Info