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. 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. Annotation 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. .comprehensive mathematical and statistical summary of time-series analysis techniques geared towards climate applications accessible to readers with knowledge of college-level calculus and statistics. (Computers and Geosciences) A key part of the book that separates it from other time series works is the explicit discussion of time uncertainty a very useful text for those wishing to understand how to analyse climate time series. (Journal of Time Series Analysis) outstanding. One of the best books on advanced practical time series analysis I have seen. (David J. Hand, Past-President Royal Statistical Society)" Front Matter....Pages i-xxxiv Front Matter....Pages 1-1 Introduction....Pages 3-32 Persistence Models....Pages 33-64 Bootstrap Confidence Intervals....Pages 65-110 Front Matter....Pages 111-111 Regression I....Pages 113-176 Spectral Analysis....Pages 177-227 Extreme Value Time Series....Pages 229-282 Front Matter....Pages 283-283 Correlation....Pages 285-338 Regression II....Pages 339-380 Front Matter....Pages 381-381 Future Directions....Pages 383-395 Back Matter....Pages 397-474 This book presents bootstrap resampling as a computationally intensive method able to meet the challenges posed by the complexities of analysing climate data. It shows how the bootstrap performs reliably in the most important statistical estimation techniques.
