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Hurricane Climatology. A Modern Statistical Guide Using R
James B. Elsner and Thomas H. Jagger
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Últimas novedades matemáticas
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Computer code is included in the text Code can be run to create all the figures and maps that are shown 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 explains how to make use of R to better understand the topic. The book is organized into two parts, the first of which provides material on software, statistics, and data. The second part presents methods and models used in hurricane climate research. |
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I Software, Statistics, and Data 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 2.2.1 Small amounts 2.2.2 Functions 2.2.3 Vectors 2.2.4 Structured data 2.2.5 Logic 2.2.6 Imports 2.3 Tables and Plots 3 Classical Statistics 3.1 Descriptive Statistics 3.2 Probability and Distributions 3.3 One-Sample Tests 3.4 Wilcoxon Signed-Rank Test 3.5 Two-Sample Tests 3.6 Statistical Formula 3.7 Compare Variances 3.8 Two-Sample Wilcoxon Test 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 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 6.2 Annual Aggregation 6.3 Coastal County Winds 6.4 NetCDF Files II 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 211 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 Intensity 9.4 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 Damage A Functions, Packages, and Data A.1 Functions A.2 Packages A.3 Data Sets B Install Package From Source
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