In this paper, we use wavelets in a Bayesian context to identify changes in the pattern of data collected over time in the presence of noise and missing observations in the data. A Bayesian analysis based on the wavelet coefﬁcients applying lifting is discussed to identify change points. Based on a simulation study, recommendations are made on the choice of lifting wavelet coefﬁcients in the presence of noise and missing observations using an adaptive lifting technique. We apply our algorithm to a real data problem where change points are already known to illustrate our recommendations
Digital Object Identifier (DOI)
"A New Approach to Bayesian Change Point Detection Using Lifting Wavelet Transform,"
Journal of Statistics Applications & Probability: Vol. 6:
3, Article 1.
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol6/iss3/1