By Manfred Mudelsee
Climate is a paradigm of a fancy process. Analysing weather info is an exhilarating problem, that is elevated by way of non-normal distributional form, serial dependence, asymmetric spacing and timescale uncertainties. This ebook provides bootstrap resampling as a computing-intensive procedure capable of meet the problem. It indicates the bootstrap to accomplish reliably within the most crucial statistical estimation strategies: 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 checks the accuracy of the algorithms via Monte Carlo experiments. It analyses a wide array of weather time sequence, giving an in depth account at the facts and the linked climatological questions.
“….comprehensive mathematical and statistical precis of time-series research innovations 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 helpful textual content for these wishing to appreciate how you can examine weather time series.”
(Journal of Time sequence Analysis)
“…outstanding. the most effective books on complicated useful time sequence research i've got seen.” (David J. Hand, Past-President Royal Statistical Society)
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Extra resources for Climate Time Series Analysis: Classical Statistical and Bootstrap Methods
Monte Carlo experiment, Pearson’s and Spearman’s correlation coefficients with Fisher’s z-transformation for bivariate lognormal AR(1) processes: calibrated CI coverage performance . . . . . . . . . . . . . . . . . . . . . Monte Carlo experiment, Pearson’s and Spearman’s correlation coefficients with Fisher’s z-transformation for bivariate lognormal AR(1) processes: average calibrated CI length.. . . . . . . . . . . . . .
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) ..
7); in (b) and (c) using a harmonic filter (section “Harmonic Filter” in Chap. 5); in (d) and (k) using the running median (Figs. 17); in (i) using nonparametric regression (Fig. 14); in (j) using a combination of a ramp model in the early and a sinusoidal in the late part (Fig. 6 Background Material 19 (“Technical Issues”) informs about details such as numerical accuracy and software implementations; it gives also Internet references where the computer programs implementing the method can be obtained.