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商品の説明
内容説明
Climate is a paradigm of a complex system. Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. This book presents bootstrap resampling as a computing-intensive method able to meet the challenge. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation. This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. It tests the accuracy of the algorithms by means of Monte Carlo experiments. It analyses a large array of climate time series, giving a detailed account on the data and the associated climatological questions. This makes the book self-contained for graduate students and researchers.
著者について
Manfred Mudelsee received his diploma in Physics from the University of Heidelberg and his doctoral degree in Geology from the University of Kiel. He was then postdoc in Statistics at the University of Kent at Canterbury, research scientist in Meteorology at the University of Leipzig and visiting scholar in Earth Sciences at Boston University; currently he does climate research at the Alfred Wegener Institute for Polar and Marine Research, Bremerhaven. His science focuses on climate extremes, time series analysis and mathematical simulation methods. He has authored over 50 peer-reviewed articles. In his 2003 Nature paper, Mudelsee introduced the bootstrap method to flood risk analysis. In 2005, he founded the company Climate Risk Analysis.
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Excellent book
2012/3/9
The book by Manfred Mudelsee is a comprehensive description of the modern statistical methods in time series analysis. It does what the title says: provides the overview of the methods of time series analysis in climatology, which is one of the most important fields with applications for time series analysis. In most branches of climatology there are observational records (time series), with the underlying dynamical system being unknown - here time series analysis is one of the few (and maybe even the only) ways of studying such records. This is why the methodology is so important, and this is why a good book on this topic, as the one written by Mudelsee, is so very important.
This book is mathematically advanced, but it is written in clear and accessible language, with directed logic and is easy to follow through all chapters (I know how many science books I abandoned after several good initial chapters then followed by overly specialised applications that were interesting only to authors).
Obviously, it is not a book for "general public": it is not fiction. But I believe it is a book suitable for a very general scientific audience interested in time series analysis and/or climatology, because it provides a modern outlook of the methodology and solid analysis for records that may be of general origin (for instance, researchers working with medical records may well benefit from the book). It could be especially valuable for undergraduate and postgraduate students studying climatology, dynamical systems and time series analysis, because it contains especially useful for beginners practicals and clear algorithm for estimation of confidence intervals and uncertainties. What is particularly important is that the uncertainties are estimated in both data and time scale, which is a serious issue in studying paleorecords.
The book is an excellent introduction into contemporary time series analysis for students and a useful compendium for the active researchers in the field. The book is not cheap. But it is worth its price once you recognize that it will serve you a long time with:
(1) algorithms ready to implement on your computer,
(2) many references to groundbreaking work in climatology and statistics and
(3) a fresh, multidisciplinary look on climate!
This book is mathematically advanced, but it is written in clear and accessible language, with directed logic and is easy to follow through all chapters (I know how many science books I abandoned after several good initial chapters then followed by overly specialised applications that were interesting only to authors).
Obviously, it is not a book for "general public": it is not fiction. But I believe it is a book suitable for a very general scientific audience interested in time series analysis and/or climatology, because it provides a modern outlook of the methodology and solid analysis for records that may be of general origin (for instance, researchers working with medical records may well benefit from the book). It could be especially valuable for undergraduate and postgraduate students studying climatology, dynamical systems and time series analysis, because it contains especially useful for beginners practicals and clear algorithm for estimation of confidence intervals and uncertainties. What is particularly important is that the uncertainties are estimated in both data and time scale, which is a serious issue in studying paleorecords.
The book is an excellent introduction into contemporary time series analysis for students and a useful compendium for the active researchers in the field. The book is not cheap. But it is worth its price once you recognize that it will serve you a long time with:
(1) algorithms ready to implement on your computer,
(2) many references to groundbreaking work in climatology and statistics and
(3) a fresh, multidisciplinary look on climate!
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