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Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height.

Author: Erik Vanem; Elzbieta Maria Bitner-Gregersen; Christopher K Wikle
Publisher: Dordrecht : Springer, 2013.
Series: Ocean engineering & oceanography.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data. This monograph is a research book and it is partly cross-disciplinary. The  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Vanem, Erik.
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height.
Dordrecht : Springer, ©2013
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Erik Vanem; Elzbieta Maria Bitner-Gregersen; Christopher K Wikle
ISBN: 9783642302534 364230253X 9783642302527 3642302521
OCLC Number: 863823161
Notes: 5.3.2 Critical Model Assumptions.
Description: 1 online resource (274 pages).
Contents: Foreword; Foreword; Preface; Acknowledgments; Contents; Acronyms; 1 Introduction and Background; 1.1 Climate Change; 1.1.1 Weather and Climate; 1.1.2 A Brief History of Climatic Research; 1.1.3 The Physical Mechanisms of Anthropogenic Global Warming; 1.1.4 Emission Scenarios; 1.1.5 Impacts: The Ocean Wave Climate; 1.1.6 Ocean Waves and Maritime Safety; 1.2 Stochastic Modeling of Environmental Processes; 1.2.1 Probabilistics Versus Deterministics; 1.2.2 Bayesian Hierarchical Space-Time Models; 1.2.3 Waves as Stochastic Processes; 1.3 Data; 1.4 Some Identified Areas for Further Research. 2.3.9 Stochastic Diffusion Models2.3.10 Regional Frequency Analysis; 2.4 Selecting a Modeling Approach; 2.5 Wave Climate Projections; 2.5.1 Climate Change; 2.5.2 Current Trends in the Wave Climate; 2.5.3 Projections of Future Trends in the Wave Climate; 2.6 Summary and Conclusions; References; 3 A Bayesian Hierarchical Space-Time Model for Significant Wave Height; 3.1 Data and Area to be Considered; 3.1.1 Data Description; 3.1.2 Area Description; 3.1.3 Initial Data Analysis; 3.2 Model Description; 3.2.1 Main Model; 3.2.2 Model Alternatives; 3.2.3 Prior Distributions on the Model Parameters. 3.3 Model Comparison and Selection3.3.1 Sum of Squares of the Residuals; 3.3.2 Loss Functions Based on Predictive Power; 3.4 Implementation and Simulations; 3.5 Results and Predictions; 3.5.1 Results for Monthly Data; 3.5.2 Results for Daily Data; 3.5.3 Results for Monthly Maximum Data; 3.5.4 Model Comparison and Selection; 3.5.5 Future Projections; 3.5.6 General Comments; 3.6 Discussion; 3.7 Summary and Conclusions; References; 4 Including a Log-Transform of the Data; 4.1 Introduction and Motivation; 4.2 Re-Transformation Bias Correction; 4.3 Revised Model Description. 4.3.1 Prior Distributions4.3.2 Loss Functions for Model Comparison; 4.4 Simulations and Results; 4.4.1 Results for Monthly Data; 4.4.2 Results for Daily Data; 4.4.3 Results for Monthly Maximum Data; 4.4.4 Simulations on 6-Hourly Data; 4.4.5 Model Comparison and Selection; 4.4.6 Future Projections; 4.5 Discussion; 4.5.1 Semi-Annual Seasonal Component; 4.6 Summary and Conclusions; References; 5 CO2 Regression Component for Future Projections; 5.1 Introduction and Background; 5.2 Data Description; 5.2.1 Historic Data; 5.2.2 Future Projections; 5.3 Model Extension; 5.3.1 Model Alternatives.
Series Title: Ocean engineering & oceanography.

Abstract:

This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence structures and uncertainties in the data. This monograph is a research book and it is partly cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, that methodology has been applied to a problem.

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