Written for climatologists and applied statisticians, this book explains the bootstrap algorithms (including novel adaptions) and methods for confidence interval construction. The accuracy of the algorithms is tested by means of Monte Carlo experiments.

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 applicationsaccessible 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 uncertaintya 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)

Introduces the bootstrap approach, which relies on modern computer power, for extracting quantitative climatological information Describes software implementation of the methods and supplies real-world examples Provides statistical background and an up-to-date overview of similar applications in Earth sciences Includes supplementary material: sn.pub/extras

Autorentext

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.

Inhalt

Part I: Fundamental Concepts.- 1 Introduction.- 2 Persistence Models.- 3 Bootstrap Confidence Intervals.- Part II: Univariate Time Series.- 4 Regression I.- 5 Spectral Analysis.- 6. Extreme Value Time Series.- Part III: Bivariate Time Series.- 7 Correlation.- 8 Regression II.- Part IV: Outlook.- 9 Future Directions.