New PDF release: Climate Time Series Analysis: Classical Statistical and

By Manfred Mudelsee

ISBN-10: 3319044494

ISBN-13: 9783319044491

ISBN-10: 3319044508

ISBN-13: 9783319044507

Climate is a paradigm of a fancy process. Analysing weather facts is an exhilarating problem, that is elevated through non-normal distributional form, serial dependence, asymmetric spacing and timescale uncertainties. This booklet provides bootstrap resampling as a computing-intensive technique in a position to meet the problem. It indicates the bootstrap to accomplish reliably within the most crucial statistical estimation concepts: 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 by way of Monte Carlo experiments. It analyses a wide array of weather time sequence, giving a close account at the facts 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 e-book that separates it from different time sequence works is the specific dialogue of time uncertainty…a very invaluable textual content for these wishing to appreciate easy methods to examine weather time series.”
(Journal of Time sequence Analysis)

“…outstanding. the best books on complex 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

Example text

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 .. . . . . . . . . . . Monte Carlo experiment, linear OLS regression with ARFIMA(0; ı; 0) noise of normal shape, even spacing .. . . . . Errors and spread of time values for dated proxy time series . . Monte Carlo experiment, linear OLS regression with timescale errors and AR(1) noise of normal shape: CI coverage performance, slope .

Davis (1986) is a textbook written by a geologist; Wilks (2006) and von Storch and Zwiers (1999) were written by climatologists. The latter three contain parts on time series analysis. 6 Background Material 23 Fig. 15 Plain-light photomicrograph from a polished section of stalagmite S3 from southern Oman. U/Th dating of samples and the seasonally varying monsoon precipitation pattern in the geographic region suggest that the laminae are annual. Dark (bright) layers reflect a higher (lower) density of pores and fluid inclusions (Fleitmann 2001).

Notation .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monte Carlo experiment, moving block bootstrap adaption to uneven spacing . . . . . . . . . . . . . . . . . . . . 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 .

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Climate Time Series Analysis: Classical Statistical and Bootstrap Methods by Manfred Mudelsee


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