Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB provides an important guide and comparison of powerful new software packages that are now widely used in research publications, including JAGS, Stan, Nimble, and TMB. It provides a gentle introduction to the most exciting specialist software that is often used to conduct cutting-edge research, along with Bayesian statistics and frequentist statistics with its maximum likelihood estimation method. In addition, this book is simple and accessible, allowing…mehr
Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS/Nimble, Stan and TMB provides an important guide and comparison of powerful new software packages that are now widely used in research publications, including JAGS, Stan, Nimble, and TMB. It provides a gentle introduction to the most exciting specialist software that is often used to conduct cutting-edge research, along with Bayesian statistics and frequentist statistics with its maximum likelihood estimation method. In addition, this book is simple and accessible, allowing researchers to carry out and understand statistical modeling. Through examples, the book covers the underlying statistical models widely used by scientists across many disciplines. Thus, this book will be useful for anyone who needs to quickly become proficient in statistical modeling, and in the model-fitting engines covered.
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.
Inhaltsangabe
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
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
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Shop der buecher.de GmbH & Co. KG Bürgermeister-Wegele-Str. 12, 86167 Augsburg Amtsgericht Augsburg HRA 13309
