Hurricane climatology : a modern statistical guide using R / James B. Elsner and Thomas H. Jagger.

Includes bibliographical references and index.

Print version record.

Cover -- Contents -- Preface -- Part One: Data, Statistics, and Software -- 1. Hurricanes, Climate, and Statistics -- 1.1. Hurricanes -- 1.2. Climate -- 1.3. Statistics -- 1.4. R -- 1.5. Organization -- 2. R Tutorial -- 2.1. Introduction -- 2.2. Data -- 2.3. Tables and Plots -- 3. Classical Statistics -- 3.1. Descriptive Statistics -- 3.2. Probability and Distributions -- 3.3. One-Sample Test -- 3.4. Wilcoxon Signed-Rank Test -- 3.5. Two-Sample Test -- 3.6. Statistical Formula -- 3.7. Two-Sample Wilcoxon Test -- 3.8. Compare Variances

""3.9. Correlation""""3.10. Linear Regression""; ""3.11. Multiple Linear Regression""; ""4. Bayesian Statistics""; ""4.1. Learning about the Proportion of Landfalls""; ""4.2. Inference""; ""4.3. Credible Interval""; ""4.4. Predictive Density""; ""4.5. Is Bayes�s Rule Needed?""; ""4.6. Bayesian Computation""; ""5. Graphs and Maps""; ""5.1. Graphs""; ""5.2. Time Series""; ""5.3. Maps""; ""5.4. Coordinate Reference Systems""; ""5.5. Export""; ""5.6. Other Graphic Packages""; ""6. Data Sets""; ""6.1. Best-Tracks Data""; ""6.2. Annual Aggregation""; ""6.3. Coastal County Winds""

6.4. NetCDF FilesPart Two: Models and Methods -- 7. Frequency Models -- 7.1. Counts -- 7.2. Environmental Variables -- 7.3. Bivariate Relationships -- 7.4. Poisson Regression -- 7.5. Model Predictions -- 7.6. Forecast Skill -- 7.7. Nonlinear Regression Structure -- 7.8. Zero-Inflated Count Model -- 7.9. Machine Learning -- 7.10. Logistic Regression -- 8. Intensity Models -- 8.1. Lifetime Highest Intensity -- 8.2. Fastest Hurricane Winds -- 8.3. Categorical Wind Speeds by County -- 9. Spatial Models -- 9.1. Track Hexagons -- 9.2. SST Data

9.3. SST and Intensity9.4. Spatial Autocorrelation -- 9.5. Spatial Regression Models -- 9.6. Spatial Interpolation -- 10. Time Series Models -- 10.1. Time Series Overlays -- 10.2. Discrete Time Series -- 10.3. Change Points -- 10.4. Continuous Time Series -- 10.5. Time-Series Network -- 11. Cluster Models -- 11.1. Time Clusters -- 11.2. Spatial Clusters -- 11.3. Feature Clusters -- 12. Bayesian Models -- 12.1. Long-Range Outlook -- 12.2. Seasonal Model -- 12.3. Consensus Model -- 12.4. Space-Time Model -- 13. Impact Models -- 13.1. Extreme Losses

13.2. Future Wind DamageAppendix A.R Functions -- Appendix B.R Packages -- Appendix C. Data sets -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- V -- W -- Z

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 R software 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 explai.

English.

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