736 pages, 220 black & white illustrations, 199 black & white line drawings
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.
From the reviews of the first edition:
"Books entirely devoted to time-series analysis of geological data are few. Univariate Time Series in Geosciences a" Theory and Examples is therefore a welcome addition. a ] A number of very useful algorithms are presented throughout the book, and students will definitely find many of them useful. In conclusion, the book contains a wealth of information and therefore will be definitely useful for the students and researchers of applied mathematics and statistics a ] ." (Rajat Mazumder, Journal of Sedimentary Research, November, 2006)
From the Contents: Introduction * Stationary Stochastic Processes * Linear Models for the Expectation Function * Interpolation * Linear Processes * Fourier Transforms of Deterministic Functions * Fourier Representation of a Stationary Stochastic Process * Does a Periodogram Estimate a Spectrum?- Estimators for a Continuous Spectrum * Estimators for a Spectrum Having a Discrete Part * Index
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