Likelihood Methods in Biology and Ecology A Modern Approach to Statistics
Likelihood Methods in Biology and Ecology: A Modern Approach to Statistics emphasizes the importance of the likelihood function in statistical theory and applications and discusses it in the context of biology and ecology. Bayesian and frequentist methods both use the likelihood function and provide differing but related insights. This is examined here both through review of basic methodology and also the integrated use of these approaches in case studies.
- Discusses the likelihood function in both Bayesian and frequentist contexts.
- Reviews and discusses standard methods of data analysis, model selection and statistical analysis, and how to apply and interpret them in real world situations.
- Examines the application of statistical methods to observed data in the context of case studies drawn from biology and ecology.
- Uniquely discusses frequentist and Bayesian approaches to statistics as complementary allowing many standard approaches to be presented in a single book.
- Poses questions to ask when planning the design and analysis of a study or experiment.
This book is written for applied researchers, scientists, consultants, statisticians and applied scientists. Although it uses examples drawn from biology, the methods here can be applied to a wide variety of research areas and provides an accessible handbook of available statistical methods for scientific settings where there is an assumed theoretical model that can be represented using a likelihood function.
"The book under review is targeted at applied scientists with a focus on explaining the applications of different statistical methods based on the likelihood... This book provides particular emphasis on the interpretability aspects of statistics for its use by an applied scientist for the analysis of their research data without a
background in statistics or mathematics. To me, the book mostly succeeds to fulfil this objective. Although it is surely difficult to provide a comprehensive overview of all modelling issues and the corresponding statistical methods under one book of around 200 pages, the author has done a pretty good job of covering the most important issues related to likelihood based inference... In this book, the author has avoided the question of superiority of inferiority of any particular statistical paradigm and discussed the applications of both frequentist and Bayesian methods simultaneously to answer a scientific question by combining both results... All of the case studies are nicely present to provide ideas of different issues in a data analysis process and their solutions based on likelihood based statistical procedures... Overall, this is a great effort in the difficult task of writing a book on statistical inferences for applied scientists from different disciplines and I want to thank the author for such a great job."
- Abhik Ghosh, ISCB December 2019