Hurricane climatology : a modern statistical guide using R

著者

    • Elsner, James B.
    • Jagger, Thomas H.

書誌事項

Hurricane climatology : a modern statistical guide using R

James B. Elsner and Thomas H. Jagger

Oxford University Press, c2013

  • : hardcover

大学図書館所蔵 件 / 全2件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 363-369) and index

内容説明・目次

内容説明

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.

目次

  • 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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