Verfügbarkeit: Hurricane climatology :

Hurricane climatology :: a modern statistical guide using R /

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 so...

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Hauptverfasser:	Elsner, James B. (VerfasserIn), Jagger, Thomas H. (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	New York : Oxford University Press, [2013]
Schlagworte:	Hurricanes > Forecasting > Statistical methods. R (Computer program language) Ouragans > Prévision > Méthodes statistiques. R (Langage de programmation) SCIENCE > Earth Sciences > Meteorology & Climatology. Electronic book.
Online-Zugang:	Volltext
Zusammenfassung:	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.
Beschreibung:	1 online resource (xiv, 373 pages) : illustrations (some color)
Bibliographie:	Includes bibliographical references and index.
ISBN:	9780199827640 0199827648 1299600832 9781299600836 0197563198 9780197563199 0199324069 9780199324064

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245	1	0	\|a Hurricane climatology : \|b a modern statistical guide using R / \|c James B. Elsner and Thomas H. Jagger.
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505	0		\|a 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
505	8		\|a ""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 Bayesâ€?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""
505	8		\|a 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
505	8		\|a 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
505	8		\|a 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
520			\|a 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.
546			\|a English.
650		0	\|a Hurricanes \|x Forecasting \|x Statistical methods.
650		0	\|a R (Computer program language) \|0 http://id.loc.gov/authorities/subjects/sh2002004407
650		6	\|a Ouragans \|x Prévision \|x Méthodes statistiques.
650		6	\|a R (Langage de programmation)
650		7	\|a SCIENCE \|x Earth Sciences \|x Meteorology & Climatology. \|2 bisacsh
650		7	\|a R (Computer program language) \|2 fast
655		4	\|a Electronic book.
700	1		\|a Jagger, Thomas H., \|e author.
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Datensatz im Suchindex

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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 Bayesâ€?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
ctrlnum	(OCoLC)843880776
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discipline	Geologie / Paläontologie
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Jagger.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">New York :</subfield><subfield code="b">Oxford University Press,</subfield><subfield code="c">[2013]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (xiv, 373 pages) :</subfield><subfield code="b">illustrations (some color)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">data file</subfield></datafield><datafield tag="504" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references and index.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Print version record.</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">""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 Bayesâ€?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""</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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spelling	Elsner, James B., author. Hurricane climatology : a modern statistical guide using R / James B. Elsner and Thomas H. Jagger. New York : Oxford University Press, [2013] 1 online resource (xiv, 373 pages) : illustrations (some color) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file 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 Bayesâ€?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. Hurricanes Forecasting Statistical methods. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Ouragans Prévision Méthodes statistiques. R (Langage de programmation) SCIENCE Earth Sciences Meteorology & Climatology. bisacsh R (Computer program language) fast Electronic book. Jagger, Thomas H., author. has work: Hurricane climatology (Text) https://id.oclc.org/worldcat/entity/E39PCGCxFKttF6KCwdg7jtp7BK https://id.oclc.org/worldcat/ontology/hasWork Print version: Elsner, James B. Hurricane climatology. New York : Oxford University Press, [2013] 9780199827633 (DLC) 2012019317 (OCoLC)798263824 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=578601 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=578601 Volltext
spellingShingle	Elsner, James B. Jagger, Thomas H. Hurricane climatology : a modern statistical guide using R / 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 Bayesâ€?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 Forecasting Statistical methods. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Ouragans Prévision Méthodes statistiques. R (Langage de programmation) SCIENCE Earth Sciences Meteorology & Climatology. bisacsh R (Computer program language) fast
subject_GND	http://id.loc.gov/authorities/subjects/sh2002004407
title	Hurricane climatology : a modern statistical guide using R /
title_auth	Hurricane climatology : a modern statistical guide using R /
title_exact_search	Hurricane climatology : a modern statistical guide using R /
title_full	Hurricane climatology : a modern statistical guide using R / James B. Elsner and Thomas H. Jagger.
title_fullStr	Hurricane climatology : a modern statistical guide using R / James B. Elsner and Thomas H. Jagger.
title_full_unstemmed	Hurricane climatology : a modern statistical guide using R / James B. Elsner and Thomas H. Jagger.
title_short	Hurricane climatology :
title_sort	hurricane climatology a modern statistical guide using r
title_sub	a modern statistical guide using R /
topic	Hurricanes Forecasting Statistical methods. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Ouragans Prévision Méthodes statistiques. R (Langage de programmation) SCIENCE Earth Sciences Meteorology & Climatology. bisacsh R (Computer program language) fast
topic_facet	Hurricanes Forecasting Statistical methods. R (Computer program language) Ouragans Prévision Méthodes statistiques. R (Langage de programmation) SCIENCE Earth Sciences Meteorology & Climatology. Electronic book.
url	https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=578601
work_keys_str_mv	AT elsnerjamesb hurricaneclimatologyamodernstatisticalguideusingr AT jaggerthomash hurricaneclimatologyamodernstatisticalguideusingr

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