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Climate Time Series Analysis: Classical Statistical and Bootstrap Methods (Atmospheric and Oceanographic Sciences Library, 42, Band 42) Gebundene Ausgabe – 1. September 2010

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Produktbeschreibungen

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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.

Über den Autor und weitere Mitwirkende

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.

Produktinformation

  • Herausgeber ‏ : ‎ Springer; 2010. Edition (1. September 2010)
  • Sprache ‏ : ‎ Englisch
  • Gebundene Ausgabe ‏ : ‎ 508 Seiten
  • ISBN-10 ‏ : ‎ 9048194814
  • ISBN-13 ‏ : ‎ 978-9048194810
  • Abmessungen ‏ : ‎ 15.6 x 2.86 x 23.4 cm
  • Kundenrezensionen:
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Research Fields

============

● Statistical Analysis of Climate Data

● Risk Analysis

● Mathematical Simulation Methods

Career

=====

● since 10/2007

Visiting/Research Scientist, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany

● since 07/2009

Eingetragener Kaufmann (HRA 201394, Amtsgericht Hannover, Germany)

● since 01/2005

CEO and Founder, Climate Risk Analysis

● 03/2011–06/2011

Guest Scientist, MARUM – Center for Marine Environmental Sciences, University of Bremen, Germany

● 09/2003–08/2004

Visiting Scholar, Department of Earth Sciences, Boston University, USA

● 09/1999–09/2007

Research Scientist, Institute of Meteorology, University of Leipzig, Germany

● 09/1997–08/1999

Postdoc, Institute of Mathematics and Statistics, University of Kent, Canterbury, United Kingdom

● 04/1996–08/1997

Research Fellow, Geological Institute, University of Kiel, Germany

Education

========

● 03/1996

PhD in Geology (magna cum laude), University of Kiel, Germany; Advisor: K. Stattegger

● 06/1990

Diploma in Physics, University of Heidelberg, Germany; Advisor: A. Mangini

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