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Record Nr. |
UNINA9910154898703321 |
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Autore |
Acevedo Mejia Sebastian |
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Titolo |
Gone with the Wind : : Estimating Hurricane and Climate Change Costs in the Caribbean / / Sebastian Acevedo Mejia |
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Pubbl/distr/stampa |
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Washington, D.C. : , : International Monetary Fund, , 2016 |
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ISBN |
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9781475544787 |
1475544782 |
9781475544817 |
1475544812 |
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Descrizione fisica |
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1 online resource (41 pages) : illustrations, tables |
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Collana |
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Disciplina |
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Soggetti |
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Climatic changes - Caribbean Area - Mathematical models |
Hurricanes - Economic aspects - Caribbean Area - Mathematical models |
Gross domestic product - Caribbean Area - Mathematical models |
Environmental Economics |
Natural Disasters |
Environmental Conservation and Protection |
Energy |
Valuation of Environmental Effects |
Climate |
Natural Disasters and Their Management |
Global Warming |
Criteria for Decision-Making under Risk and Uncertainty |
Economywide Country Studies: Latin America |
Caribbean |
Alternative Energy Sources |
Natural disasters |
Climate change |
Environmental management |
Environment |
Greenhouse gas emissions |
Renewable energy |
Climatic changes |
Greenhouse gases |
Renewable energy sources |
Antigua and Barbuda |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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This paper studies the economic costs of hurricanes in the Caribbean by constructing a novel dataset that combines a detailed record of tropical cyclones’ characteristics with reported damages. I estimate the relation between hurricane wind speeds and damages in the Caribbean; finding that the elasticity of damages to GDP ratio with respect to maximum wind speeds is three in the case of landfalls. The data show that hurricane damages are considerably underreported, particularly in the 1950s and 1960s, with average damages potentially being three times as large as the reported average of 1.6 percent of GDP per year. I document and show that hurricanes that do not make landfall also have considerable negative impacts on the Caribbean economies. Finally, I estimate that the average annual hurricane damages in the Caribbean will increase between 22 and 77 percent by the year 2100, in a global warming scenario of high CO2 concentrations and high global temperatures. |
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2. |
Record Nr. |
UNINA9911019797303321 |
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Autore |
Molenberghs Geert |
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Titolo |
Missing data in clinical studies / / Geert Molenberghs, Michael G. Kenward |
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Pubbl/distr/stampa |
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Chichester, Eng. ; ; Hoboken, NJ, : J. Wiley & Sons, c2007 |
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ISBN |
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9786610839506 |
9781280839504 |
1280839503 |
9780470510445 |
0470510447 |
9780470510438 |
0470510439 |
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Descrizione fisica |
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1 online resource (528 p.) |
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Collana |
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Altri autori (Persone) |
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KenwardMichael G. <1956-> |
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Disciplina |
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Soggetti |
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Clinical trials - Statistical methods |
Missing observations (Statistics) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references (p. 483-496) and index. |
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Nota di contenuto |
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Missing Data in Clinical Studies; Contents; Preface; Acknowledgements; I Preliminaries; 1 Introduction; 1.1 From Imbalance to the Field of Missing Data Research; 1.2 Incomplete Data in Clinical Studies; 1.3 MAR, MNAR, and Sensitivity Analysis; 1.4 Outline of the Book; 2 Key Examples; 2.1 Introduction; 2.2 The Vorozole Study; 2.3 The Orthodontic Growth Data; 2.4 Mastitis in Dairy Cattle; 2.5 The Depression Trials; 2.6 The Fluvoxamine Trial; 2.7 The Toenail Data; 2.8 Age-Related Macular Degeneration Trial; 2.9 The Analgesic Trial; 2.10 The Slovenian Public Opinion Survey |
3 Terminology and Framework3.1 Modelling Incompleteness; 3.2 Terminology; 3.3 Missing Data Frameworks; 3.4 Missing Data Mechanisms; 3.5 Ignorability; 3.6 Pattern-Mixture Models; Part II Classical Techniques and the Need for Modelling; 4 A Perspective on Simple Methods; 4.1 Introduction; 4.1.1 Measurement model; 4.1.2 Method for handling missingness; 4.2 Simple Methods; 4.2.1 Complete case analysis; 4.2.2 Imputation methods; 4.2.3 Last observation carried |
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forward; 4.3 Problems with Complete Case Analysis and Last Observation Carried Forward |
4.4 Using the Available Cases: a Frequentist versus a Likelihood Perspective4.4.1 A bivariate normal population; 4.4.2 An incomplete contingency table; 4.5 Intention to Treat; 4.6 Concluding Remarks; 5 Analysis of the Orthodontic Growth Data; 5.1 Introduction and Models; 5.2 The Original, Complete Data; 5.3 Direct Likelihood; 5.4 Comparison of Analyses; 5.5 Example SAS Code for Multivariate Linear Models; 5.6 Comparative Power under Different Covariance Structures; 5.7 Concluding Remarks; 6 Analysis of the Depression Trials; 6.1 View 1: Longitudinal Analysis |
6.2 Views 2a and 2b and All versus Two Treatment ArmsIII Missing at Random and Ignorability; 7 The Direct Likelihood Method; 7.1 Introduction; 7.2 Ignorable Analyses in Practice; 7.3 The Linear Mixed Model; 7.4 Analysis of the Toenail Data; 7.5 The Generalized Linear Mixed Model; 7.6 The Depression Trials; 7.7 The Analgesic Trial; 8 The Expectation-Maximization Algorithm; 8.1 Introduction; 8.2 The Algorithm; 8.2.1 The initial step; 8.2.2 The E step; 8.2.3 The M step; 8.3 Missing Information; 8.4 Rate of Convergence; 8.5 EM Acceleration; 8.6 Calculation of Precision Estimates |
8.7 A Simple Illustration8.8 Concluding Remarks; 9 Multiple Imputation; 9.1 Introduction; 9.2 The Basic Procedure; 9.3 Theoretical Justification; 9.4 Inference under Multiple Imputation; 9.5 Efficiency; 9.6 Making Proper Imputations; 9.7 Some Roles for Multiple Imputation; 9.8 Concluding Remarks; 10 Weighted Estimating Equations; 10.1 Introduction; 10.2 Inverse Probability Weighting; 10.3 Generalized Estimating Equations for Marginal Models; 10.3.1 Marginal models for non-normal data; 10.3.2 Generalized estimating equations; 10.3.3 A method based on linearization |
10.4 Weighted Generalized Estimating Equations |
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Sommario/riassunto |
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Missing Data in Clinical Studies provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents the reader with approaches to effectively address them. The text provides a critique of conventional and simple methods before moving on to discuss more advanced approaches. The authors focus on practical and modeling concepts, providing an extensive set of case studies to illustrate the problems described. Provides a practical guide to the analysis of clinical trials and related studies with missing data.Examines |
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