Inbunden, Engelska, 2001
Del i serien Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Analysis of Time Series Structure
SSA and Related Techniques
Av Nina Golyandina, Vladimir Nekrutkin, Anatoly A Zhigljavsky
3969 kr
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Beskrivning
Produktinformation
- Utgivningsdatum: 2001-01-23
- Mått: 156 x 234 x 22 mm
- Vikt: 750 g
- Format: Inbunden
- Språk: Engelska
- Serie: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
- Antal sidor: 320
- Förlag: Taylor & Francis Inc
- ISBN: 9781584881940
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Recensioner i media
"[This] is the first book on SSA aimed at statisticians. The authors provide clear and concise descriptions of the basic methodology of this new technique, and this is a welcome reference text for time series practitioners. … This book provides the background to successfully understand and intelligently apply SSA. I strongly recommend it to anyone interested in time series analysis."- Journal of the American Statistical Association"This book summarizes the results published on SSA in the last 15 years. It is a good source of SSA methodology for scientists who wish to complement classical procedures for time series analysis by SSA tools."- Mathematical Reviews, Issue 2002"…the formal mathematical theory, which underpins the method, is laid out with admirable clarity. The authors have performed a service to the statistical community by writing this book. It is likely to become the standard reference to SSA; helpful to the applied statistician who wishes to analyse a times series and also to the theoretician who may wish to develop this interesting approach to time series analysis further."Short Book Reviews of the ISI"The present monograph is dedicated to a recently proposed technique, singular spectrum analysis (SSA), that can be considered an extension of Pearson's problem to the situation in which the points in the space correspond to realizations of a time series…it is an important contribution to a modern area that is becoming increasingly needed in problems of electrical engineering, economics, meteorology, oceanography, and other fields. The authors should be commended for bringing this method to the attention of the statistical community."-Andrew L. Rukhin, University of Maryland at Baltimore County for Technometrics, August 2002
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