By Manfred Mudelsee
Climate is a paradigm of a fancy procedure. Analysing weather info is a thrilling problem, that is 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 capable of meet the problem. It indicates the bootstrap to accomplish reliably within the most crucial statistical estimation thoughts: regression, spectral research, severe values and correlation.
This publication is written for climatologists and utilized statisticians. It explains step-by-step the bootstrap algorithms (including novel adaptions) and techniques for self assurance 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 a close account at the information and the linked climatological questions.
“….comprehensive mathematical and statistical precis of time-series research options 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 ebook that separates it from different time sequence works is the specific dialogue of time uncertainty…a very worthy textual content for these wishing to appreciate tips to examine weather time series.”
(Journal of Time sequence Analysis)
“…outstanding. the best books on complicated functional time sequence research i've got seen.” (David J. Hand, Past-President Royal Statistical Society)
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Additional info for Climate Time Series Analysis: Classical Statistical and Bootstrap Methods
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 . . . . . . . . . . 1 List of Tables Monte Carlo experiment, linear OLS regression with AR(2) noise of normal shape, even spacing .. . . . . . . .
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 variable” refers to the climatic variations recorded by the variations in the proxy variable. The ability of a proxy variable to indicate a climate variable depends on the characteristic timescales (between resolution and time range). For example, ı 18 O variations in benthic foraminifera over timescales of only a few decades do not record ice-volume variations (which are slower). T /, in relation to the uncertainty associated with the pure measurement for the time series analysed here.