By Manfred Mudelsee
Climate is a paradigm of a fancy procedure. Analysing weather facts is an exhilarating problem, that's elevated by means of non-normal distributional form, serial dependence, asymmetric spacing and timescale uncertainties. This publication offers bootstrap resampling as a computing-intensive strategy in a position to meet the problem. It exhibits the bootstrap to accomplish reliably within the most crucial statistical estimation concepts: regression, spectral research, severe values and correlation.
This ebook is written for climatologists and utilized statisticians. It explains step-by-step the bootstrap algorithms (including novel adaptions) and strategies for self belief period development. It exams the accuracy of the algorithms by way of Monte Carlo experiments. It analyses a wide array of weather time sequence, giving an in depth account at the information and the linked climatological questions.
“….comprehensive mathematical and statistical precis of time-series research strategies geared in the direction of weather applications…accessible to readers with wisdom of college-level calculus and statistics.” (Computers and Geosciences)
“A key a part of the publication that separates it from different time sequence works is the categorical dialogue of time uncertainty…a very priceless textual content for these wishing to appreciate the right way to examine weather time series.”
(Journal of Time sequence Analysis)
“…outstanding. the most effective books on complex useful time sequence research i've got seen.” (David J. Hand, Past-President Royal Statistical Society)
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Extra info for Climate Time Series Analysis: Classical Statistical and Bootstrap Methods
3 7 66 67 68 70 84 89 96 Monte Carlo experiment, linear OLS regression with AR(1) noise of normal shape, even spacing: CI coverage performance . . . . . . . . . . . . . . . . . . . . . 119 Monte Carlo experiment, linear OLS regression with AR(1) noise of normal shape, even spacing: average CI length . . . . . . . . . . . . . . . .. . . . . . . . . . 119 Monte Carlo experiment, linear OLS regression with AR(1) noise of lognormal shape, even spacing .
1993) 1=2 f As air temperature indicator; own determination of MSE (Eq. 8) after Jouzel et al. (2007: Fig. S4 therein) g Average MAD value (Figs. 6745) h Relative error (Röthlisberger et al. 2000) i Trace substances are part of a complex system, involving variations at the source, during transport (wind) and at deposition; they represent a more local or regional climate signal j O / (Fig. i k Reimer et al. (2004) l 14 C in tree-rings on yearly to decadal resolution has a (small) proxy error as atmospheric 14 C indicator because the wood formation is not constant (the major portion is formed in spring and early summer) and because tree-ring thickness varies (Stuiver et al.
Grade correlation coefficient, bivariate lognormal distribution .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . Monte Carlo experiment, linear errors-in-variables regression with AR(1) noise of normal shape and complete prior knowledge: CI coverage performance . . . . . . Monte Carlo experiment, linear errors-in-variables regression with AR(1) noise of normal shape and complete prior knowledge: CI coverage performance (continued) .. .
Climate Time Series Analysis: Classical Statistical and Bootstrap Methods by Manfred Mudelsee