Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists (eBook)

Howard B. Stauffer (Autor)

eBook Download: PDF

2007 | 1. Auflage
448 Seiten
John Wiley & Sons (Verlag)
978-0-470-18507-0 (ISBN)

Infos

The first all-inclusive introduction to modern statistical research
methods in the natural resource sciences

The 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 strategiesâEUR"the a posteriori
model selection strategy and the a priori parsimonious model
selection strategy using AIC and DICâEUR"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.

Autor

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

Inhalt

Preface.

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 S-PlusA? and R Orientation I: Introduction.

1.7 S-Plus and R Orientation II: Distributions.

1.8 S-Plus and R Orientation III: Estimation of Mean and Proportion, Sampling Error, and Confidence Intervals.

1.9 S-Plus 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 Analysisa??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 Information-Theoretic 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 Information-Theoretic 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 S-Plus 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 S-Plus or R and WinBUGS Applications.

6.7 Summary.

Problems.

7. Introduction to Mixed-Effects Modeling.

7.1 Introduction.

7.2 Dependent Datasets.

7.3 Linear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R.

7.4 Nonlinear Mixed-Effects Modeling: Frequentist Statistical Analysis in S-Plus and R.

7.5 Conclusions: Frequentist Statistical Analysis in S-Plus and R.

7.6 Mixed-Effects 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 Mixed-Effects 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 Least-Square Fit: The Linear Regression Model.

A.3 Linear Regression and Multiple Linear Regression Statistics.

A.4 Stepwise Multiple Linear Regression Methods.

A.5 Best-Subsets Selection Multiple Linear Regression.

A.6 Goodness of Fit.

Appendix B. Answers to Problems.

References.

Index.

Rezensionen

?The book provides case studies illustrating common problems that
exist in natural resource sciences, and presents the statistical
knowledge and tools needed for a modem treatment of these issues.?
(APADE, 2009)

"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 often-neglected 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 real-life situations."
(CHOICE May 2008)

"An ideal text for natural resource students studying
statistical research methods at the upper-undergraduate or graduate
level and also service as a valuable problem-solving guide."
(Mathematical Reviews 2008)

Produkt-Details

• Erscheint lt. Verlag: 28.6.2008
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Naturwissenschaften
• Themenwelt: Technik
• Schlagworte: Angewandte Wahrscheinlichkeitsrechnung u. Statistik • Applied Probability & Statistics • Bauingenieur- u. Bauwesen • Civil Engineering & Construction • Environmental Science • Environmental Studies • Statistics • Statistik • Umweltforschung • Umweltwissenschaften • Wasserwirtschaft • water resources
• ISBN-10: 0-470-18507-4 / 0470185074
• ISBN-13: 978-0-470-18507-0 / 9780470185070