Applied Statistical Modelling for Ecologists: A Practical Guide to Bayesian and Likelihood Inference Using R, JAGS, NIMBLE, Stan and TMB

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. Provides a comprehensive, applied introduction to some of the most exciting, cutting-edge model fitting software packages: JAGS, Nimble, Stan, and TMBCovers all the basics of the modern applied statistical modeling that have become a key part of any natural science, including linear, generalized linear, mixed and also hierarchical modelsProvides applied introduction to the two dominant methods of parametric statistical modeling: maximum likelihood and Bayesian inferenceAdopts what could be called a Rosetta stone approach, wherein understanding of one software, and of its associated language, will be greatly enhanced by seeing the analogous code in one of the other engines INDICE: 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

• ISBN: 978-0-443-13715-0
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 520
• Fecha Publicación: 26/07/2024
• Nº Volúmenes: 1
• Idioma: Inglés