Analyzing Environmental Data (eBook)

Walter W. Piegorsch, University of South Carolina, Columbia, South Carolina, USA Walter W. Piegorsch earned an M.S. and a Ph.D. Statistics at the Biometrics Unit, Cornell University. He was a Statistician with the U.S. National Institute of Environmental Health Sciences from 1984 to 1993, then moved to the University of South Carolina, Columbia, where he is now Professor and Director of Undergraduate Studies in Statistics. Walter has co-authored or co-edited two books, Statistics for Environmental Biology and Toxicology with A. John Bailer, and Case Studies in Environmental Statistics with Douglas W. Nychka and Lawrence H. Cox. He also serves or has served as a member of the Editorial Board of Environmental and Molecular Mutagenesis and Mutation Research, the Editorial Review Board of Environmental Health Perspectives, and as an Associate Editor for Environmetrics, Environmental and Ecological Statistics, Biometrics, and the Journal of the American Statistical Association. Walter is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has received a Distinguished Achievement Medal from the American Statistical Association Section on Statistics and the Environment. He has served as Vice-Chair of the American Statistical Association Council of Sections Governing Board, as Program Chairman of the Joint Statistical Meetings, and as Secretary of the Eastern North American Region of the International Biometric Society. He has also served and continues to serve on advisory boards and peer review groups for governmental agencies including the U.S. National Toxicology Program, the U.S. Environmental Protection Agency, and the U.S. National Science Foundation.

Preface.

1 Linear regression.

1.1 Simple linear regression.

1.2 Multiple linear regression.

1.3 Qualitative predictors: ANOVA and ANCOVA models.

1.4 Random-effects models.

1.5 Polynomial regression.

Exercises.

2 Nonlinear regression.

2.1 Estimation and testing.

2.2 Piecewise regression models.

2.3 Exponential regression models.

2.4 Growth curves.

2.5 Rational polynomials.

2.6 Multiple nonlinear regression.

Exercises.

3 Generalized linear models.

3.1 Generalizing the classical linear model.

3.2 Theory of generalized linear models.

3.3 Specific forms of generalized linear models.

Exercises.

4 Quantitative risk assessment with stimulus-response
data.

4.1 Potency estimation for stimulus-response data.

4.2 Risk estimation.

4.3 Benchmark analysis.

4.4 Uncertainty analysis.

4.5 Sensitivity analysis.

4.6 Additional topics.

Exercises.

5 Temporal data and autoregressive modeling.

5.1 Time series.

5.2 Harmonic regression.

5.3 Autocorrelation.

5.4 Autocorrelated regression models.

5.5 Simple trend and intervention analysis.

5.6 Growth curves revisited.

Exercises.

6 Spatially correlated data.

6.1 Spatial correlation.

6.2 Spatial point patterns and complete spatial randomness.

6.3 Spatial measurement.

6.4 Spatial prediction.

Exercises.

7 Combining environmental information.

7.1 Combining P-values.

7.2 Effect size estimation.

7.3 Meta-analysis.

7.4 Historical control information.

Exercises.

8 Fundamentals of environmental sampling.

8.1 Sampling populations - simple random sampling.

8.2 Designs to extend simple random sampling.

8.3 Specialized techniques for environmental sampling.

Exercises.

A Review of probability and statistical inference.

A.1 Probability functions.

A.2 Families of distributions.

A.3 Random sampling.

A.4 Parameter estimation.

A.5 Statistical inference.

A.6 The delta method.

B Tables.

References.

Author index.

Subject index.

"This book covers an impressive range of topics . . . The book can
be used as a basis for courses of different levels." (Stat Papers,
2010)



"Some of the unique aspects of Piegorsch and Bailer's
treatment are benchmark dose estimation for toxicants, statistical
issues in risk assessment, the assessment of trend and step changes
in temporal data, and the discussion of sampling." (Journal of
the American Statistical Association, June 2008)

"I enjoyed reading this book and I recommend it to those readers
interested in the field of environmental statistics." (Journal
of Applied Statistics, January 2009)

"This highly recommended book will provide the background for
the proper application of statistical methods. These will make an
invaluable contribution to the realistic assessment of the damage
to the environment to be expected as a result of global warming.
The subject and author indexes are both excellent." (Journal of
Chemical Technology and Biotechnology, August 2006)

"This highly recommended book will provide the background for
the proper application of statistical methods. These will make an
invaluable contribution to the realistic assessment of the damage
to the environment to be expected as a result of global warming.
The subject and author indexes are both excellent." (Journal of
Chemical Technology and Biotechnology, Aug 2008)

"...This is a substantial and thorough book...a handy reference
book for any statistician's bookshelf..." (International
Statistical Institute, January 2006)