Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists
Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists
ISBN: 9780470185094
Apr 2007
400 pages
Description
The first allinclusive introduction to modern statistical research methods in the natural resource sciencesThe use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixedeffects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, handson treatment of realworld examples, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists successfully introduces the key concepts of statistical analysis and inference with an accessible, easytofollow approach.
The book provides case studies illustrating common problems that exist in the natural resource sciences and presents the statistical knowledge and tools needed for a modern treatment of these issues. Subsequent chapter coverage features:

An introduction to the fundamental concepts of Bayesian statistical analysis, including its historical background, conjugate solutions, Bayesian hypothesis testing and decisionmaking, and Markov Chain Monte Carlo solutions

The relevant advantages of using Bayesian statistical analysis, rather than the traditional frequentist approach, to address research problems

Two alternative strategiesâ€”the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DICâ€”to model selection and inference

The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression

An introduction to mixedeffects modeling in SPlusÂ® and R for analyzing natural resource data sets with varying error structures and dependencies
Each statistical concept is accompanied by an illustration of its frequentist application in SPlusÂ® or R as well as its Bayesian application in WinBUGS. Brief introductions to these software packages are also provided to help the reader fully understand the concepts of the statistical methods that are presented throughout the book. Assuming only a minimal background in introductory statistics, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists is an ideal text for natural resource students studying statistical research methods at the upperundergraduate or graduate level and also serves as a valuable problemsolving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.
1. Introduction.
1.1 Introduction.
1.2 Three Case Studies.
1.3 Overview of Some Solution Strategies.
1.4 Review: Principles of Project Management.
1.5 Applications.
1.6 SPlusÂ® and R Orientation I: Introduction.
1.7 SPlus and R Orientation II: Distributions.
1.8 SPlus and R Orientation III: Estimation of Mean and Proportion, Sampling Error, and Confidence Intervals.
1.9 SPlus and R Orientation IV: Linear Regression.
1.10 Summary.
Problems.
2. Bayesian Statistical Analysis I: Introduction.
2.1 Introduction.
2.2 Three Methods for Fitting Models to Datasets.
2.3 The Bayesian Paradigm for Statistical Inference: Bayes Theorem.
2.4 Conjugate Priors.
2.5 Other Priors.
2.6 Summary.
Problems.
3. Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision theory.
3.1 Bayesian Hypothesis Testing: Bayes Factors.
3.2 Bayesian Decision Theory.
3.3 Preview: More Advanced Methods of Bayesian Statistical Analysisâ??Markov Chain Monte Carlo (MCMC) Algorithms and WinBUGS Software.
3.4 Summary.
Problems.
4. Bayesian Statistical Inference III: MCMC Algorithms and WinBUGS Software Applications.
4.1 Introduction.
4.2 Markov Chain Theory.
4.3 MCMC Algorithms.
4.4 WinBUGS Applications.
4.5 Summary.
Problems.
5. Alternative Strategies for Model Selection and Inference Using InformationTheoretic Criteria.
5.1 Alternative Strategies for Model Selection and Influence: Descriptive and Predictive Model Selection.
5.2 Descriptive Model Selection: A Posteriori Exploratory Model Selection and Inference.
5.3 Predictive Model Selection: A Priori Parsimonious Model Selection and Inference Using InformationTheoretic Criteria.
5.4 Methods of Fit.
5.5 Evaluation of Fit: Goodness of Fit.
5.6 Model Averaging.
5.7 Applications: Frequentist Statistical Analysis in SPlus and R; Bayesian Statistical Analysis in WinBUGS.
5.8 Summary.
Problems.
6. An Introduction to Generalized Linear Models: Logistic Regression Models.
6.1 Introduction to Generalized Linear Models (GLMs).
6.2 GLM Design.
6.3 GLM Analysis.
6.4 Logistic Regression Analysis.
6.5 Other Generalized Linear Models (GLMs).
6.6 SPlus or R and WinBUGS Applications.
6.7 Summary.
Problems.
7. Introduction to MixedEffects Modeling.
7.1 Introduction.
7.2 Dependent Datasets.
7.3 Linear MixedEffects Modeling: Frequentist Statistical Analysis in SPlus and R.
7.4 Nonlinear MixedEffects Modeling: Frequentist Statistical Analysis in SPlus and R.
7.5 Conclusions: Frequentist Statistical Analysis in SPlus and R.
7.6 MixedEffects Modeling: Bayesian Statistical Analysis in WinBUGS.
7.7 Summary.
Problems.
8. Summary and Conclusions.
8.1 Summary of Solutions to Chapter 1 Case Studies.
8.2 Appropriate Application of Statistics in the Natural Resource Sciences.
8.3 Statistical Guidelines for Design of Sample Surveys and Experiments.
8.4 Two Strategies for Model Selection and Inference.
8.5 Contemporary Methods for Statistical Analysis I: Generalized Linear Modeling and MixedEffects Modeling.
8.6 Contemporary Methods in Statistical Analysis II: Bayesian Statistical Analysis Using MCMC Methods with WinBUGS Software.
8.7 Concluding Remarks: Effective Use of Statistical Analysis and Inference.
8.8 Summary.
Appendix A. review of Linear regression and Multiple Linear regression Analysis.
A.1 Introduction.
A.2 LeastSquare Fit: The Linear Regression Model.
A.3 Linear Regression and Multiple Linear Regression Statistics.
A.4 Stepwise Multiple Linear Regression Methods.
A.5 BestSubsets Selection Multiple Linear Regression.
A.6 Goodness of Fit.
Appendix B. Answers to Problems.
References.
Index.
""The book's strength lie in the choice of material, the explication of methods and use, and detail of the code provided ? The bottom line is this book is useful. It is designated not merely to give you a sense of these oftenneglected statistical methods but to get you up and running on them. It does a phenomenal job of that task."" (Ecology, November 2008)
""Stauffer's book seems very suitable for second statistics on modern regression modeling focusing on Bayesian thinking."" (Journal of the American Statistician, December 2008)
""Stauffer's book seems very suitable for second statistics on modern regression modeling focusing on Bayesian thinking."" (Journal of the American Statistician, Dec 2008)
""This is an excellent book presenting difficult statistical ideals by using data obtained from reallife situations."" (CHOICE May 2008)
""An ideal text for natural resource students studying statistical research methods at the upperundergraduate or graduate level and also service as a valuable problemsolving guide."" (Mathematical Reviews 2008)