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

Millar, Russell B.

Hardcover

ISBN 10: 0470094826 ISBN 13: 9780470094822

Publisher: Wiley, 2011

Synopsis

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.

About this title

About the Author

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

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.

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.

Edition details

• Publisher: Wiley
• Publication date: 2011
• Language: English
• ISBN 10: 0470094826
• ISBN 13: 9780470094822
• Binding: Hardcover
• Edition number: 1
• Number of pages: 376