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Using penalized contrasts for the change-point problem
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Research works printed but not linked: 9
- Gijbels, 1999, On the estimation of jump points in smooth curves, Ann. Inst. Statist. Math., 51, 231, 10.1023/A:1003802007064 · doi:10.1023/a:1003802007064
- Braun, 2000, Multiple change point fitting via quasilikehood with application to DNA sequence segmentation, Biometrika, 87, 301, 10.1093/biomet/87.2.301 · DOI · Paper page
- Lavielle, 1999, Detection of multiple changes in a sequence of dependent variables, Stoch. Proc. Appl., 83, 79, 10.1016/S0304-4149(99)00023-X · doi:10.1016/s0304-4149(99)00023-x
- Lavielle, 2000, The multiple change-points problem for the spectral distribution, Bernoulli, 6, 845, 10.2307/3318759 · doi:10.2307/3318759
- Yao, 1988, Estimating the number of change-points via Schwarz criterion, Statist. Probab. Lett., 6, 181, 10.1016/0167-7152(88)90118-6 · DOI · Paper page
- Birgé, 2001, Gaussian model selection, J. Eur. Math. Soc., 3, 203, 10.1007/s100970100031 · DOI · Paper page
- Delyon, 1999, Convergence of a stochastic approximation version of the EM algorithm, Ann. Statist., 27, 94, 10.1214/aos/1018031103 · DOI · Paper page
- S.M. Kay, Fundamentals of Statistical Signal Processing, vol. 2, Prentice-Hall, Englewood Cliffs, NJ, 1998.
- Lavielle, 2001, An application of MCMC methods to the multiple change-points problem, Signal Processing, 81, 39, 10.1016/S0165-1684(00)00189-4 · doi:10.1016/s0165-1684(00)00189-4
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Metadata
- Authors
- Lavielle, M.
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2005-08
- Volume
- 85
- Issue
- 8
- Pages
- 1501-1510
- ISSN
- 0165-1684 (ISSN-L)
- OpenAlex
- W2117227640
- Cited by
- 652
- References
- 0
- FWCI
- 6.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- Lavielle, M. (2005). Using penalized contrasts for the change-point problem. Signal Processing. https://doi.org/10.1016/j.sigpro.2005.01.012
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
