On‐line inference for multiple changepoint problems
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DOI: 10.1111/j.1467-9868.2007.00601.x
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Citations
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Cited by:
- Eric Ruggieri, 2018. "A pruned recursive solution to the multiple change point problem," Computational Statistics, Springer, vol. 33(2), pages 1017-1045, June.
- Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
- Chen, Yudong & Wang, Tengyao & Samworth, Richard J., 2022. "High-dimensional, multiscale online changepoint detection," LSE Research Online Documents on Economics 113665, London School of Economics and Political Science, LSE Library.
- Chao Du & Chu-Lan Michael Kao & S. C. Kou, 2016. "Stepwise Signal Extraction via Marginal Likelihood," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 314-330, March.
- Robert C Wilson & Matthew R Nassar & Joshua I Gold, 2013. "A Mixture of Delta-Rules Approximation to Bayesian Inference in Change-Point Problems," PLOS Computational Biology, Public Library of Science, vol. 9(7), pages 1-18, July.
- Yukio Ohsawa & Teruaki Hayashi & Takaaki Yoshino, 2019. "Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market," Papers 1901.09469, arXiv.org.
- Stein Olav Skrøvseth & Johan Gustav Bellika & Fred Godtliebsen, 2012. "Causality in Scale Space as an Approach to Change Detection," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-14, December.
- Christophe Andrieu & Arnaud Doucet & Roman Holenstein, 2010. "Particle Markov chain Monte Carlo methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(3), pages 269-342, June.
- Tian, Guo-Liang & Ng, Kai Wang & Li, Kai-Can & Tan, Ming, 2009. "Non-iterative sampling-based Bayesian methods for identifying changepoints in the sequence of cases of Haemolytic uraemic syndrome," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3314-3323, July.
- Hinoveanu, Laurentiu C. & Leisen, Fabrizio & Villa, Cristiano, 2019. "Bayesian loss-based approach to change point analysis," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 61-78.
- Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.
- Faicel Chamroukhi, 2016. "Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 374-411, October.
- Inder Tecuapetla-Gómez & Axel Munk, 2017. "Autocovariance Estimation in Regression with a Discontinuous Signal and m-Dependent Errors: A Difference-Based Approach," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(2), pages 346-368, June.
- Ahelegbey, Daniel Felix & Billio, Monica & Casarin, Roberto, 2024. "Modeling Turning Points in the Global Equity Market," Econometrics and Statistics, Elsevier, vol. 30(C), pages 60-75.
- Thies, Sven & Molnár, Peter, 2018. "Bayesian change point analysis of Bitcoin returns," Finance Research Letters, Elsevier, vol. 27(C), pages 223-227.
- Jian He & Asma Khedher & Peter Spreij, 2021. "A Kalman particle filter for online parameter estimation with applications to affine models," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 353-403, July.
- Ricardo C. Pedroso & Rosangela H. Loschi & Fernando Andrés Quintana, 2023. "Multipartition model for multiple change point identification," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 759-783, June.
- Yixiao Li & Gloria Lin & Thomas Lau & Ruochen Zeng, 2019. "A Review of Changepoint Detection Models," Papers 1908.07136, arXiv.org.
- Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
- Sang Gil Kang & Woo Dong Lee & Yongku Kim, 2021. "Bayesian Multiple Change-Points Detection in a Normal Model with Heterogeneous Variances," Computational Statistics, Springer, vol. 36(2), pages 1365-1390, June.
- Haipeng Xing & Hongsong Yuan & Sichen Zhou, 2017. "A Mixtured Localized Likelihood Method for GARCH Models with Multiple Change-points," Review of Economics & Finance, Better Advances Press, Canada, vol. 8, pages 44-60, May.
- Seong W. Kim & Sabina Shahin & Hon Keung Tony Ng & Jinheum Kim, 2021. "Binary segmentation procedures using the bivariate binomial distribution for detecting streakiness in sports data," Computational Statistics, Springer, vol. 36(3), pages 1821-1843, September.
- Yukio Ohsawa, 2018. "Graph-Based Entropy for Detecting Explanatory Signs of Changes in Market," The Review of Socionetwork Strategies, Springer, vol. 12(2), pages 183-203, December.
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