Maximum Likelihood Estimation and Inference (PDF)

This book takes a fresh look at the popular and well-established
method of maximum likelihood for statistical estimation and
inference. It begins with an intuitive introduction to the concepts
and background of likelihood, and moves through to the latest
developments in maximum likelihood methodology, including general
latent variable models and new material for the practical
implementation of integrated likelihood using the free ADMB
software. Fundamental issues of statistical inference are also
examined, with a presentation of some of the philosophical debates
underlying the choice of statistical paradigm.

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.