Description
Book SynopsisThis book provides a coherent introduction to intermediate and advanced methods for environmental data analysis and is based on a course which the author has taught for many years. It prepares students for careers in environmental analysis centered on statistics and allied quantitative methods of evaluation.
Trade Review"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)
Table of ContentsPreface.
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.
