Univariate Time Series in Geosciences

Theory and Examples
1. Auflage. Softcover version of original hardcover Auflage 2006
ISBN: 978-3-642-06276-6
Verlag: Springer

Buch, Englisch, 718 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 1095 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Gilgen, Hans

Fachgebiete

• Geowissenschaften Geologie Meteorologie, Klimatologie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Geowissenschaften Geologie Geologie
• Geowissenschaften Umweltwissenschaften Umweltüberwachung, Umweltanalytik, Umweltinformatik
• Geowissenschaften Geologie GIS, Geoinformatik
• Geowissenschaften Geologie Geophysik
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Naturwissenschaften Physik Angewandte Physik Geophysik

Inhaltsverzeichnis

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.