Univariate Time Series in Geosciences Theory and Examples /

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 ea...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Gilgen, Hans (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 03064nam a22005175i 4500
001 978-3-540-30968-0
003 DE-He213
005 20151204145027.0
007 cr nn 008mamaa
008 100301s2006 gw | s |||| 0|eng d
020 |a 9783540309680  |9 978-3-540-30968-0 
024 7 |a 10.1007/3-540-30968-3  |2 doi 
040 |d GrThAP 
050 4 |a QC801-809 
072 7 |a PHVG  |2 bicssc 
072 7 |a SCI032000  |2 bisacsh 
082 0 4 |a 550  |2 23 
082 0 4 |a 526.1  |2 23 
100 1 |a Gilgen, Hans.  |e author. 
245 1 0 |a Univariate Time Series in Geosciences  |h [electronic resource] :  |b Theory and Examples /  |c by Hans Gilgen. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2006. 
300 |a XVIII, 718 p. 220 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a 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. 
520 |a 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. 
650 0 |a Earth sciences. 
650 0 |a Geophysics. 
650 0 |a Atmospheric sciences. 
650 0 |a Computer simulation. 
650 0 |a Physics. 
650 1 4 |a Earth Sciences. 
650 2 4 |a Geophysics/Geodesy. 
650 2 4 |a Earth Sciences, general. 
650 2 4 |a Atmospheric Sciences. 
650 2 4 |a Simulation and Modeling. 
650 2 4 |a Numerical and Computational Physics. 
650 2 4 |a Environmental Monitoring/Analysis. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540238102 
856 4 0 |u http://dx.doi.org/10.1007/3-540-30968-3  |z Full Text via HEAL-Link 
912 |a ZDB-2-EES 
950 |a Earth and Environmental Science (Springer-11646)