Analyzing Environmental Data - A John Bailer

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Auteur(s) : A John Bailer - Walter W Piegorsch

Editeur : John Wiley & Sons

Langue : Anglais

Parution : 01/03/2005

Format : Moyen, de 350g à 1kg

Nombre de pages : 512.0

Expédition : 1007

Dimensions : 25.2 x 17.4 x 3.5

ISBN : 9780470848364

Résumé :
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Biographie:

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.

A. John Bailer, Department of Mathematics & Statistics, Miami University, USA.

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Sommaire:

Preface xiii

1 Linear regression 1

1.1 Simple linear regression 2

1.2 Multiple linear regression 10

1.3 Qualitative predictors: ANOVA and ANCOVA models 16

1.3.1 ANOVA models 16

1.3.2 ANCOVA models 20

1.4 Random-effects models 24

1.5 Polynomial regression 26

Exercises 31

2 Nonlinear regression 41

2.1 Estimation and testing 42

2.2 Piecewise regression models 44

2.3 Exponential regression models 55

2.4 Growth curves 65

2.4.1 Gompertz model 66

2.4.2 Logistic growth curves 69

2.4.3 Weibull growth curves 79

2.5 Rational polynomials 83

2.5.1 Michaelis-Menten model 83

2.5.2 Morgan-Mercer-Flodin model 87

2.6 Multiple nonlinear regression 89

Exercises 91

3 Generalized linear models 103

3.1 Generalizing the classical linear model 104

3.1.1 Non-normal data and the exponential class 104

3.1.2 Linking the mean response to the predictor variables 106

3.2 Theory of generalized linear models 107

3.2.1 Estimation via maximum likelihood 108

3.2.2 Deviance function 109

3.2.3 Residuals 112

3.2.4 Inference and model assessment 113

3.2.5 Estimation via maximum quasi-likelihood 116

3.2.6 Generalized estimating equations 117

3.3 Specific forms of generalized linear models 121

3.3.1 Continuous/homogeneous-variance data GLiMs 121

3.3.2 Binary data GLiMs (including logistic regression) 124

3.3.3 Overdispersion: extra-binomial variability 135

3.3.4 Count data GLiMs 141

3.3.5 Overdispersion: extra-Poisson variability 149

3.3.6 Continuous/constant-CV data GLiMs 152

Exercises 158

4 Quantitative risk assessment with stimulus-response data 171

4.1 Potency estimation for stimulus-response data 172

4.1.1 Median effective dose 172

4.1.2 Other levels of effective dose 176

4.1.3 Other potency measures 178

4.2 Risk estimation 180

4.2.1 Additional risk and extra risk 180

4.2.2 Risk at low doses 187

4.3 Benchmark analysis 190

4.3.1 Benchmark dose estimation 190

4.3.2 Confidence limits on benchmark dose 192

4.4 Uncertainty analysis 193

4.4.1 Uncertainty factors 194

4.4.2 Monte Carlo methods 196

4.5 Sensitivity analysis 200

4.5.1 Identifying sensitivity to input variables 200

4.5.2 Correlation ratios 204

4.5.3 Identifying sensitivity to model assumptions 206

4.6 Additional topics 206

Exercises 207

5 Temporal data and autoregressive modeling 215

5.1 Time series 215

5.2 Harmonic regression 216

5.2.1 Simple harmonic regression 217

5.2.2 Multiple harmonic regression 221

5.2.3 Identifying harmonics: Fourier analysis 221

5.3 Autocorrelation 233

5.3.1 Testing for autocorrelation 233

5.3.2 The autocorrelation function 235

5.4 Autocorrelated regression models 239

5.4.1 AR models 239

5.4.2 Extensions: MA, ARMA, and ARIMA 241

5.5 Simple trend and intervention analysis 242

5.5.1 Simple linear trend 243

5.5.2 Trend with seasonality 243

5.5.3 Simple intervention at a known time 248

5.5.4 Change in trend at a known time 249

5.5.5 Jump and change in trend at a known time 249

5.6 Growth curves revisited 254

5.6.1 Longitudinal growth data 254

5.6.2 Mixed models for growth curves 255

Exercises 264

6 Spatially correlated data 275

6.1 Spatial correlation 275

6.2 Spatial point patterns and complete spatial randomness 276

6.2.1 Chi-square tests 277