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.
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