Abstract
We introduce a new robust Bayesian change-point analysis in the presence of outliers. We employ an idea of general posterior based on density power divergence combined with horseshoe prior for differences of underlying signals. A posterior computation algorithm is proposed using Markov chain Monte Carlo. The proposed method is demonstrated through simulation and real data analysis.
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The authors would like to thank the referees for the careful reading of the paper, and the valuable suggestions and comments. This work is partially supported by Japan Society for Promotion of Science (KAKENHI) grant numbers 18K12757 and 17K14233.
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Sugasawa, S., Hashimoto, S. (2021). Robust Bayesian Changepoint Analysis in the Presence of Outliers. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 238. Springer, Singapore. https://doi.org/10.1007/978-981-16-2765-1_39
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DOI: https://doi.org/10.1007/978-981-16-2765-1_39
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