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Author Country (or Countries)

USA

Abstract

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

Suggested Reviewers

N/A

Digital Object Identifier (DOI)

http://dx.doi.org/10.18576/jsap/060301

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