Applied Statistical Modelling for Ecologists
A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB
Published on 18. July 2024
Book
Paperback/Softback
550 pages
978-0-443-13715-0 (ISBN)
Description
**2025 PROSE Award Finalist in Environmental Science**
Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.
This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kery, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.
The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.
Applied Statistical Modelling for Ecologists provides a gentle introduction to the essential models of applied statistics: linear models, generalized linear models, mixed and hierarchical models. All models are fit with both a likelihood and a Bayesian approach, using several powerful software packages widely used in research publications: JAGS, NIMBLE, Stan, and TMB. In addition, the foundational method of maximum likelihood is explained in a manner that ecologists can really understand.
This book is the successor of the widely used Introduction to WinBUGS for Ecologists (Kery, Academic Press, 2010). Like its parent, it is extremely effective for both classroom use and self-study, allowing students and researchers alike to quickly learn, understand, and carry out a very wide range of statistical modelling tasks.
The examples in Applied Statistical Modelling for Ecologists come from ecology and the environmental sciences, but the underlying statistical models are very widely used by scientists across many disciplines. This book will be useful for anybody who needs to learn and quickly become proficient in statistical modelling, with either a likelihood or a Bayesian focus, and in the model-fitting engines covered, including the three latest packages NIMBLE, Stan, and TMB.
More details
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Dimensions
Height: 233 mm
Width: 187 mm
Thickness: 27 mm
Weight
1136 gr
ISBN-13
978-0-443-13715-0 (9780443137150)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Marc Kéry | Kenneth F. Kellner
Applied Statistical Modelling for Ecologists
A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB
E-Book
07/2024
Elsevier
€77.95
Available for download
Persons
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences. Dr. Ken Kellner is an Assistant Research Professor at Michigan State University, MI, United States. Prior to his current position, he completed a Ph.D. in forest ecology at Purdue University, IN, United States, and a postdoc at West Virginia University, WV, United States. Ken's research has covered a wide range of topics including forest management, plant demography, and avian and mammal conservation. He has published this research in more than 40 peer reviewed publications. In addition, Ken is particularly focused on the development of open-source software tools for ecological modeling. He has developed or contributed to several software packages that are widely used by ecologists and featured in several books, including the successful R packages jagsUI, unmarked, and ubms.
Content
1. Introduction
2. Introduction to statistical inference
3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models
4. Introduction to general-purpose model-fitting engines and the model of the mean
5. Simple linear regression with Normal errors
6. Comparison of two groups
7. Comparisons among multiple groups
8. Comparisons in two classifications or with two categorical covariates
9. General linear model with continuous and categorical explanatory variables
10. Linear mixed-effects model
11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression
12. Overdispersion, zero-inflation and offsets in a GLM
13. Poisson regression with both continuous and categorical explanatory variables
14. Poisson mixed-effects model or Poisson GLMM
15. Comparing two groups in a Binomial regression
16. Binomial GLM with both continuous and categorical explanatory variables
17. Binomial mixed-effects model or Binomial GLMM
18. Model building, model checking and model selection
19. General hierarchical models: Site-occupancy species distribution model (SDM)
20. Integrated models
21. Conclusion
2. Introduction to statistical inference
3. Linear regression models and their extensions to generalized linear, hierarchical and integrated models
4. Introduction to general-purpose model-fitting engines and the model of the mean
5. Simple linear regression with Normal errors
6. Comparison of two groups
7. Comparisons among multiple groups
8. Comparisons in two classifications or with two categorical covariates
9. General linear model with continuous and categorical explanatory variables
10. Linear mixed-effects model
11. Introduction to the Generalized linear model (GLM): Comparing two groups in a Poisson regression
12. Overdispersion, zero-inflation and offsets in a GLM
13. Poisson regression with both continuous and categorical explanatory variables
14. Poisson mixed-effects model or Poisson GLMM
15. Comparing two groups in a Binomial regression
16. Binomial GLM with both continuous and categorical explanatory variables
17. Binomial mixed-effects model or Binomial GLMM
18. Model building, model checking and model selection
19. General hierarchical models: Site-occupancy species distribution model (SDM)
20. Integrated models
21. Conclusion