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Parametric Statistical Change Point Analysis
Abstract
This work is an in-depth study of the change point problem from a general point of view and a further examination of change point analysis of the most commonly used statistical models Change point problems are encountered in such disciplines as economics, finance, medicine, psychology, signal processing, and geology, to mention only several The exposition is clear and systematic, with a great deal of introductory material included Different models are presented in each chapter, including gamma and exponential models, rarely examined thus far in the literature Other models covered in detail are the multivariate normal, univariate normal, regression, and discrete models Extensive examples throughout the text emphasize key concepts and different methodologies are used, namely the likelihood ratio criterion, and the Bayesian and information criterion approaches A comprehensive bibliography and two indices complete the study
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Cited by
A Review and Comparison of Changepoint Detection Techniques for Climate Data
Reeves, J., Chen, J., Wang, X. L., Lund, R., & Lu, Q. Q. (2007) · Journal of Applied Meteorology and Climatology · doi:10.1175/jam2493.1 · Cited by 638
Self-citationUsing statistical anomaly detection models to find clinical decision support malfunctions
Ray, S., McEvoy, D. S., Aaron, S., Hickman, T.-T., & Wright, A. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy041 · Cited by 44
Metadata
- Authors
- Chen, J., & Gupta, A. K.
- Venue
- Birkhäuser Boston eBooks
- Publisher
- Birkhäuser Boston
- Published
- 2000
- OpenAlex
- W4298025466
- Cited by
- 284
- References
- 0
- FWCI
- 2.65
- Type
- book
- Language
- en
- Open access
- closed
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- Chen, J., & Gupta, A. K. (2000). Parametric Statistical Change Point Analysis. Birkhäuser Boston eBooks. https://doi.org/10.1007/978-1-4757-3131-6
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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.