Hurricane Climatology: A Modern Statistical Guide Using R by James B. ElsnerHurricane Climatology: A Modern Statistical Guide Using R by James B. Elsner

Hurricane Climatology: A Modern Statistical Guide Using R

byJames B. Elsner, Thomas H. Jagger

Hardcover | March 19, 2013

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Hurricanes are nature's most destructive storms and they are becoming more powerful as the globe warms. Hurricane Climatology explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity. It uses the open-source and now widely used Rsoftware for statistical computing to create a tutorial-style manual for independent study, review, and reference. The text is written around the code that when copied will reproduce the graphs, tables, and maps. The approach is different from other books that use R. It focuses on a single topic and explains how to make use of R to better understand the topic. The book is organized into two parts, the first ofwhich provides material on software, statistics, and data. The second part presents methods and models used in hurricane climate research.
James B. Elsner is the Earl and Sophia Shaw Professor of Geography at Florida State University where he teaches applied spatial statistics and hurricane climatology. Dr. Elsner is President and CEO of Climatek; a company that develops software for hurricane-risk models. His research interests include the changing nature of hurricane ri...
Title:Hurricane Climatology: A Modern Statistical Guide Using RFormat:HardcoverDimensions:416 pages, 9.25 × 6.12 × 0.98 inPublished:March 19, 2013Publisher:OUPLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:019982763X

ISBN - 13:9780199827633


Table of Contents

I: Software, Statistics, and Data1. Hurricanes, Climate, and Statistics1.1 Hurricanes1.2 Climate1.3 Statistics1.4 R1.5 Organization2. R Tutorial2.1 Introduction2.2 Data2.2.1 Small amounts2.2.2 Functions2.2.3 Vectors2.2.4 Structured data2.2.5 Logic2.2.6 Imports2.3 Tables and Plots3. Classical Statistics3.1 Descriptive Statistics3.2 Probability and Distributions3.3 One-Sample Tests3.4 Wilcoxon Signed-Rank Test3.5 Two-Sample Tests3.6 Statistical Formula3.7 Compare Variances3.8 Two-Sample Wilcoxon Test3.9 Correlation3.10 Linear Regression3.11 Multiple Linear Regression4. Bayesian Statistics4.1 Learning About the Proportion of Landfalls4.2 Inference4.3 Credible Interval4.4 Predictive Density4.5 Is Bayes Rule Needed?4.6 Bayesian Computation5. Graphs and Maps5.1 Graphs5.2 Time series5.3 Maps5.4 Coordinate Reference Systems5.5 Export5.6 Other Graphic Packages6. Data Sets6.1 Best-Tracks6.2 Annual Aggregation6.3 Coastal County Winds6.4 NetCDF FilesII: Models and Methods7. Frequency Models7.1 Counts7.2 Environmental Variables7.3 Bivariate Relationships7.4 Poisson Regression7.5 Model Predictions7.6 Forecast Skill7.7 Nonlinear Regression Structure7.8 Zero-Inflated Count Model7.9 Machine Learning7.10 Logistic Regression8. Intensity Models 2118.1 Lifetime Highest Intensity8.2 Fastest Hurricane Winds8.3 Categorical Wind Speeds by County9. Spatial Models9.1 Track Hexagons9.2 SST Data9.3 SST and Intensity9.4 Spatial Autocorrelation9.5 Spatial Regression Models9.6 Spatial Interpolation10. Time Series Models10.1 Time Series Overlays10.2 Discrete Time Series10.3 Change Points10.4 Continuous Time Series10.5 Time Series Network11. Cluster Models11.1 Time Clusters11.2 Spatial Clusters11.3 Feature Clusters12. Bayesian Models12.1 Long-Range Outlook12.2 Seasonal Model12.3 Consensus Model12.4 Space-Time Model13. Impact Models13.1 Extreme Losses13.2 Future Wind DamageA Functions, Packages, and DataA.1 FunctionsA.2 PackagesA.3 Data Sets