By Manfred Mudelsee
Climate is a paradigm of a posh approach. Analysing weather information is an exhilarating problem, that is elevated by means of non-normal distributional form, serial dependence, asymmetric spacing and timescale uncertainties. This e-book provides bootstrap resampling as a computing-intensive technique in a position to meet the problem. It exhibits 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 belief period development. It assessments 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 info and the linked climatological questions.
“….comprehensive mathematical and statistical precis of time-series research suggestions 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 booklet 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 find out how to examine weather time series.”
(Journal of Time sequence Analysis)
“…outstanding. the most effective books on complex functional 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
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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 .
Monte Carlo experiment, standard deviation estimation of a Gaussian purely random process . . . . . . . . . Monte Carlo experiment, mean and median estimation of a lognormal purely random process . . . . . . . . Estimation settings (theoretical and practical) and approaches (classical and bootstrap) to solve practical problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monte Carlo experiment, mean estimation of AR(1) noise processes with uneven spacing, normal and lognormal shape .
6 xxxi Monte Carlo experiment, Spearman’s correlation coefficient with Fisher’s z-transformation for bivariate lognormal AR(1) processes: influence of block length selection . . . . . . . . . . . . . . . . . . . . . . . Monte Carlo experiment, Spearman’s correlation coefficient without Fisher’s z-transformation for bivariate lognormal AR(1) processes .. . . . .. . . . . . . . . . Monte Carlo experiment, Pearson’s correlation coefficient with Fisher’s z-transformation for bivariate lognormal AR(1) processes ..