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Case Studies in Environmental Statistics

96,29 €*

ISBN-13:
9781461222262
Veröffentl:
2012
Seiten:
196
Autor:
Douglas Nychka
Serie:
132, Lecture Notes in Statistics
eBook Typ:
PDF
eBook Format:
EPUB
Kopierschutz:
1 - PDF Watermark
Sprache:
Englisch
Beschreibung:
This book offers a set of case studies exemplifying the broad range of statis­ tical science used in environmental studies and application. The case studies can be used for graduate courses in environmental statistics, as a resource for courses in statistics using genuine examples to illustrate statistical methodol­ ogy and theory, and for courses in environmental science. Not only are these studies valuable for teaching about an essential cross-disciplinary activity but they can also be used to spur new research along directions exposed in these examples. The studies reported here resulted from a program of research carried on by the National Institute of Statistical Sciences (NISS) during the years 1992- 1996. NISS was created in 1991 as an initiative of the national statistics or­ ganizations, with the mission to renew and focus efforts of statistical science on important cross-disciplinary problems. One of NISS' first projects was a cooperative research effort with the U.S. Environmental Protection Agency (EPA) on problems of great interest to environmental science and regulation, surely one of today's most important cross-disciplinary activities. With the support and encouragement of Gary Foley, Director of the (then) U.S. EPA Atmospheric Research and Exposure Assessment Laboratory, a project and a research team were assembled by NISS that pursued a program which produced a set of results and products from which this book was drawn.
1 Introduction: Problems in Environmental Monitoring and Assessment.- 1 Statistical Methods for Environmental Monitoring and Assessment.- 2 Outline of Case Studies.- 3 Sources of Data and Software.- Acknowledgments.- 2 Modeling Ozone in the Chicago Urban Area.- 1 Introduction.- 2 Data Sources.- 3 Trend Analysis and Adjustment.- 4 Trends from Semiparametric Models.- 5 Trends in Exceedances.- 6 Summary.- Acknowledgments.- References.- 3 Regional and Temporal Models for Ozone Along the Gulf Coast.- 1 Introduction.- 2 Diurnal Variation in Ozone.- 3 Meteorological Clusters and Ozone.- 4 Regional Variation in Ozone.- 5 Summary.- 6 Future Directions.- References.- 4 Design of Air-Quality Monitoring Networks.- 1 Introduction.- 2 Data.- 3 Spatial Models.- 4 Thinning a Small Urban Network.- 5 Adding Rural Stations to Northern Illinois.- 6 Modifying Regional Networks.- 7 Scientific Contributions and Discussion.- References.- 5 Estimating Trends in the Atmospheric Deposition of Pollutants.- 1 Introduction.- 2 Monitoring Data.- 3 Case Studies.- 4 Future Research.- Acknowledgment.- References.- 6 Airborne Particles and Mortality.- 1 Introduction.- 2 Statistical Studies of Particles and Mortality.- 3 An Example: Data from Birmingham, Alabama.- 4 Results for Birmingham.- 5 Comparisons with Other Cities.- 6 Conclusions: Accidental Association or Causal Connection.- References.- 7 Categorical Exposure-Response Regression Analysis of Toxicology Experiments.- 1 Introduction.- 2 The Tetrachloroethylene Database.- 3 Statistical Models for Exposure-Response Relationships.- 4 Computing Software: CatReg.- 5 Application to Tetrachloro ethylene Data.- 6 Conclusions.- 7 Future Directions.- Acknowledgments.- References.- 8 Workshop: Statistical Methods for Combining Environmental Information.- 1 The NISS-USEPA Workshop Series.- 2 Combining Environmental Information.- 3 Combining Environmental Epidemiology Information.- 4 Combining Environmental Assessment Information.- 5 Combining Environmental Monitoring Data.- 6 Future Directions.- References.- A Appendix A: FUNFITS, Data Analysis and Statistical Tools for Estimating Functions Douglas Nychka, Perry D. Haaland, Michael A. O'Connell, Stephen Ellner.- 1 Introduction.- 2 What's So Special About FUNFITS?.- 2.1 An Example.- 3 A Basic Model for Regression.- 4 Thin-Plate Splines: tps.- 4.1 Determining the Smoothing Parameter.- 4.2 Approximate Splines for Large Data Sets.- 4.3 Standard Errors.- 5 Spatial Process Models: krig.- 5.1 Specifying the Covariance Function.- 5.2 Some Examples of Spatial Process Estimates.- Acknowledgments.- References.- B Appendix B: DI, A Design Interface for Constructing and Analyzing Spatial Designs Nancy Saltzman, Douglas Nychka.- 1 Introduction.- 2 An Example.- 3 How DI Works.- 3.1 Network Objects.- 3.2 The Design Editor.- 3.3 User Modifications.- C Appendix C: Workshops Sponsored Through the EPA/NISS Cooperative Agreement.- D Appendix D: Participating Scientists in the Cooperative Agreement.

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