Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists

The first all–inclusive introduction to modern statistical research methods in the natural resource sciences
he 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, mixed–effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands–on treatment of real–world 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, easy–to–follow 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 decision–making, 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 strategies—the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC—to model selection and inference
The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression
An introduction to mixed–effects modeling in S–Plus® 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 S–Plus® 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 upper–undergraduate or graduate level and also serves as a valuable problem–solving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.

Spis treści:
Preface.
1. Introduction.
2. Bayesian Statistical Analysis I: Introduction.
3. Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision theory.
4. Bayesian Statistical Inference III: MCMC Algorithms and WinBUGS Software Applications.
5. Alternative Strategies for Model Selection and Inference Using Information–Theoretic Criteria.
6. An Introduction to Generalized Linear Models: Logistic Regression Models.
7. Introduction to Mixed–Effects Modeling.
8. Summary and Conclusions.
Appendix A. review of Linear regression and Multiple Linear regression Analysis.
Appendix B. Answers to Problems.
References.
Index.

Nota biograficzna:

Howard B. Stauffer, PhD, is Professor of Applied Statistics and former chairperson of the Mathematics Department at Humboldt State University. Dr. Stauffer has over thirtyfive years of experience in academia, government, and industry specializing in sampling and experimental design and analysis, in addition to the current methodologies in statistical a nalysis, such as generalized linear modeling, mixed–effects modeling, Bayesian statistical analysis, and capture–recapture analysis.

Okładka tylna:

The first all–inclusive introduction to modern statistical research methods in the natural resource sciences
he 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, mixed–effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands–on treatment of real–world 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, easy–to–follow 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 decision–making, 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 strategies—the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC—to model selection and inference
The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression
An introduction to mixed–effects modeling in S–Plus® 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 S–Plus® 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 upper–undergraduate or graduate level and also serves as a valuable problem–solving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.