Maximum Likelihood Estimation and Inference

Key features:

• Provides an accessible introduction to pragmatic maximum likelihood modelling.
• Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood.
• Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data.
• Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology.
• Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB.
• Provides all program code and software extensions on a supporting website.
• Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters.

This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.

Russell B. Millar is the author of Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB, published by Wiley.