Department of Mathematics
University of Wisconsin River Falls
BAYESIAN CHANGE POINT DETECTION: A COMPARISON BETWEEN DWT AND LIFTING
We use wavelets within a Bayesian framework to identify changes in the form of shifts in data collected over time in the presence of noise and missing observations. We modify and extend an existing Bayesian change point detection procedure due to Ogden and Lynch(1999) which uses the discrete wavelet transform. Our main objective is to investigate and compare the usefulness of the two procedures: Discrete Wavelet Transform and Lifting.