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

Format:

Book

Author/Creator:

Elsner, James B.

Contributor:

Jagger, Thomas H.

Series:

Oxford scholarship online.

Oxford scholarship online

Language:

English

Subjects (All):

Hurricanes--Forecasting--Statistical methods.

Hurricanes.

R (Computer program language).

Physical Description:

1 online resource (xiv, 373 pages ) illustrations (black and white, and colour)

Edition:

1st ed.

Place of Publication:

New York : Oxford University Press, 2013.

Language Note:

English

Summary:

'Hurricane Climatology'explains how to analyze and model hurricane data to better understand and predict present and future hurricane activity.

Contents:

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 Files

Part 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 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

Appendix 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.

Notes:

Previously issued in print: 2013.

Includes bibliographical references and index.

Description based on metadata supplied by the publisher and other sources.

ISBN:

0-19-932406-9

0-19-756319-8

0-19-982764-8

OCLC:

922904430