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Multivariate Time Series Analysis with Examples in Biostatistics
(Englisch)
Giovanni Motta (Maastricht University, The Netherlands) & Hernando Ombao (University of California, Irvine, USA)

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Produktbeschreibung

Stationary Time Series. Introduction. Fitting Parametric Models to Multivariate Time Series. Spectral Analysis of Multivariate Time Series. Dimension Reduction for Multivariate Time Series. Non-stationary Time Series. Spectral Analysis of Multivariate Non-Stationary Time Series. Multi-Scale Methods for Non-Stationary Time Series. Classification, Discrimination and Clustering. Dimension Reduction for Multivariate Non-Stationary Time Series. Bayesian Non-Stationary Methods for Multivariate Time Series.


This book has been written for students and researchers who wish to gain a working knowledge of time series analysis as applied in biomedical sciences. While intended as a text for graduate and undergraduate students in statistics and biostatistics, it covers a wide range of parametric, nonparametric and multi-scale methods for the analysis of stochastic processes coming from biology, medicine, epidemiology and neuroscience. The book assumes only a basic knowledge of calculus, matrix algebra and elementary statistics.


Inhaltsverzeichnis



Stationary Time Series. Introduction. Fitting Parametric Models to Multivariate Time Series. Spectral Analysis of Multivariate Time Series. Dimension Reduction for Multivariate Time Series. Non-stationary Time Series. Spectral Analysis of Multivariate Non-Stationary Time Series. Multi-Scale Methods for Non-Stationary Time Series. Classification, Discrimination and Clustering. Dimension Reduction for Multivariate Non-Stationary Time Series. Bayesian Non-Stationary Methods for Multivariate Time Series.


Klappentext



This book has been written for students and researchers who wish to gain a working knowledge of time series analysis as applied in biomedical sciences. While intended as a text for graduate and undergraduate students in statistics and biostatistics, it covers a wide range of parametric, nonparametric and multi-scale methods for the analysis of stochastic processes coming from biology, medicine, epidemiology and neuroscience. The book assumes only a basic knowledge of calculus, matrix algebra and elementary statistics.



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