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Statistics for Long-Memory Processes.
Abstract
Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.
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Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750332965 · Cited by 2,104
Error analysis of continuous GPS position time series
Williams, S. D. P., Bock, Y., Fang, P., Jamason, P., Nikolaidis, R. M., Prawirodirdjo, L., Miller, M., & Johnson, D. J. (2004) · Journal of Geophysical Research: Solid Earth · doi:10.1029/2003jb002741 · Cited by 734
Sassi, R., Cerutti, S., Lombardi, F., Malik, M., Huikuri, H. V., Peng, C.-K., Schmidt, G., Yamamoto, Y., Reviewers:, D., Gorenek, B., et al. (2015) · Europace · doi:10.1093/europace/euv015 · Cited by 727
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Metadata
- Authors
- Ray, W. D., & Beran, J.
- Venue
- Journal of the Royal Statistical Society. Series A (Statistics in Society)
- Publisher
- Oxford University Press (OUP)
- Published
- 1996
- Volume
- 159
- Issue
- 1
- Pages
- 180
- ISSN
- 0964-1998 (ISSN-L)
- OpenAlex
- W2319085245
- Cited by
- 2,686
- References
- 0
- FWCI
- 149.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ray, W. D., & Beran, J. (1996). Statistics for Long-Memory Processes. Journal of the Royal Statistical Society. Series A (Statistics in Society). https://doi.org/10.2307/2983481
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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.