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Models for dependent time series
Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statisti...
| Autores principales: | , , |
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| Lenguaje: | eng |
| Publicado: |
CRC Press
2015
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| Materias: | |
| Acceso en línea: | http://cds.cern.ch/record/2050469 |
| _version_ | 1780948101103616000 |
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| author | Tunnicliffe Wilson, Granville Reale, Marco Haywood, John |
| author_facet | Tunnicliffe Wilson, Granville Reale, Marco Haywood, John |
| author_sort | Tunnicliffe Wilson, Granville |
| collection | CERN |
| description | Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater |
| id | cern-2050469 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2015 |
| publisher | CRC Press |
| record_format | invenio |
| spelling | cern-20504692021-04-21T20:05:48Zhttp://cds.cern.ch/record/2050469engTunnicliffe Wilson, GranvilleReale, MarcoHaywood, JohnModels for dependent time seriesMathematical Physics and MathematicsModels for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational materCRC Pressoai:cds.cern.ch:20504692015 |
| spellingShingle | Mathematical Physics and Mathematics Tunnicliffe Wilson, Granville Reale, Marco Haywood, John Models for dependent time series |
| title | Models for dependent time series |
| title_full | Models for dependent time series |
| title_fullStr | Models for dependent time series |
| title_full_unstemmed | Models for dependent time series |
| title_short | Models for dependent time series |
| title_sort | models for dependent time series |
| topic | Mathematical Physics and Mathematics |
| url | http://cds.cern.ch/record/2050469 |
| work_keys_str_mv | AT tunnicliffewilsongranville modelsfordependenttimeseries AT realemarco modelsfordependenttimeseries AT haywoodjohn modelsfordependenttimeseries |