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Maximum Likelihood Estimation and Inference - With Examples in R, SAS, and ADMB (Hardcover)
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Maximum Likelihood Estimation and Inference - With Examples in R, SAS, and ADMB (Hardcover)
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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.
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