Case studies in environmental statistics
著者
書誌事項
Case studies in environmental statistics
（Lecture notes in statistics, 132）
Springer, c1998
大学図書館所蔵 全44件
注記
"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"--Pref
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.- 1.1 Health and Environmental Effects.- 1.2 Background.- 1.3 Overview of the Case Studies.- 2 Data Sources.- 2.1 Ozone Data.- 2.2 Meteorological Data.- 3 Trend Analysis and Adjustment.- 3.1 Parametric Modeling.- 3.2 Urban Ozone in Chicago.- 3.3 Comparison with Rural Locations.- 4 Trends from Semiparametric Models.- 4.1 Semiparametric Modeling.- 4.2 Application to Chicago Urban Ozone.- 5 Trends in Exceedances.- 5.1 Exceedance Modeling.- 5.2 Modeling Exceedance Probabilities for the Chicago Urban Area.- 5.3 Modeling Excess Ozone over a Threshold.- 5.4 Prediction of Extreme Ozone Levels.- 6 Summary.- Acknowledgments.- References.- 3 Regional and Temporal Models for Ozone Along the Gulf Coast.- 1 Introduction.- 1.1 Scientific Issues.- 1.2 Data and Dimension Reduction.- 2 Diurnal Variation in Ozone.- 2.1 Singular Value Decomposition.- 2.2 Urban Ozone in Houston.- 2.3 Conclusions.- 3 Meteorological Clusters and Ozone.- 3.1 Cluster Analysis.- 3.2 Nonparametric Regression.- 3.3 Urban Ozone in Houston.- 4 Regional Variation in Ozone.- 4.1 Rotated Principal Components.- 4.2 Gulf Coast States.- 5 Summary.- 6 Future Directions.- References.- 4 Design of Air-Quality Monitoring Networks.- 1 Introduction.- 1.1 Environmental Issues.- 1.2 Why Find Spatial Predictions for Ozone.- 1.3 Designs and Data Analysis.- 1.4 Chapter Outline.- 2 Data.- 2.1 Hourly Ozone and Related Daily Summaries.- 2.2 Handling Missing Data.- 2.3 Model Output.- 3 Spatial Models.- 3.1 Random Fields.- 3.2 Spatial Estimates.- 3.3 Design Evaluation.- 4 Thinning a Small Urban Network.- 4.1 Preliminary Results.- 4.2 Designs from Subset Selection.- 4.3 Results.- 5 Adding Rural Stations to Northern Illinois.- 5.1 Space-Filling Designs.- 5.2 Results for Rural Illinois.- 6 Modifying Regional Networks.- 6.1 Results for the Larger Midwest Network.- 7 Scientific Contributions and Discussion.- 7.1 Future Directions.- References.- 5 Estimating Trends in the Atmospheric Deposition of Pollutants.- 1 Introduction.- 2 Monitoring Data.- 2.1 Case Study I.- 2.2 Case Study II.- 2.3 Additional Ongoing Monitoring.- 3 Case Studies.- 3.1 Gamma Model for Trend Estimation.- 3.2 Network Ability to Detect and Quantify Trends.- 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.- 3.1 Summary of Available Data.- 3.2 Statistical Modeling Strategy.- 4 Results for Birmingham.- 4.1 Linear Least Squares and Poisson Regression.- 4.2 Nonlinear Effects.- 4.3 Nonparametric Regression.- 5 Comparisons with Other Cities.- 5.1 Seasonal Parametric and Semiparametric Models.- 5.2 Results: Chicago.- 5.3 Results: Salt Lake County.- 5.4 Direct Comparisons Between Chicago and Birmingham.- 6 Conclusions: Accidental Association or Causal Connection.- References.- 7 Categorical Exposure-Response Regression Analysis of Toxicology Experiments.- 1 Introduction.- 1.1 Critical Exposure-Response Information and Modeling Approaches.- 1.2 Issues in Exposure-Response Risk Assessment.- 2 The Tetrachloroethylene Database.- 2.1 Severity Scoring.- 2.2 Censoring.- 3 Statistical Models for Exposure-Response Relationships.- 3.1 Haber's Law.- 3.2 Homogeneous Logistic Model.- 3.3 Stratified Regression Model.- 3.4 Marginal Modeling Approach.- 3.5 Other Issues.- 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.- 3.1 Passive Smoking.- 3.2 Nitrogen Dioxide Exposure.- 4 Combining Environmental Assessment Information.- 4.1 A Benthic Index for the Chesapeake Bay.- 4.2 Hazardous Waste Site Characterization.- 4.3 Estimating Snow Water Equivalent.- 5 Combining Environmental Monitoring Data.- 5.1 Combining P-Samples.- 5.2 Combining P- and NP-Samples.- 5.3 Combining NP-Samples.- 5.4 Combining NP-Samples Exhibiting More Than Purposive Structure.- 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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