Univariate Time Series in Geosciences: Theory and Examples

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Springer Science & Business Media, Jan 16, 2006 - Science - 718 pages
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The author introduces the statistical analysis of geophysical time series. The book includes also a chapter with an introduction to geostatistics, many examples and exercises which help the reader to work with typical problems. More complex derivations are provided in appendix-like supplements to each chapter. Readers are assumed to have a basic grounding in statistics and analysis. The reader is invited to learn actively from genuine geophysical data. He has to consider the applicability of statistical methods, to propose, estimate, evaluate and compare statistical models, and to draw conclusions.

The author focuses on the conceptual understanding. The example time series and the exercises lead the reader to explore the meaning of concepts such as the estimation of the linear time series (AMRA) models or spectra.

This book is also a guide to using "R" for the statistical analysis of time series. "R" is a powerful environment for the statistical and graphical analysis of data."R" is available under GNU conditions.

 

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Contents

Introduction
1
Stationary Stochastic Processes
39
Linear Models for the Expectation Function
121
Interpolation
171
Linear Processes
249
4
282
Fourier Transforms of Deterministic Functions
329
Fourier Representation of a Stationary Stochastic Process
441
Does a Periodogram Estimate a Spectrum?
499
Estimators for a Continuous Spectrum
527
Estimators for a Spectrum Having a Discrete Part
641
A Answers to Problems
683
References
697
Index
705
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