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Maximum Likelihood Estimation and Inference
Russell B. Millar
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Description for Maximum Likelihood Estimation and Inference
Hardcover. Applied Likelihood Methods provides an accessible and practical introduction to likelihood modeling, supported by examples and software. The book features applications from a range of disciplines, including statistics, medicine, biology, and ecology. Series: Statistics in Practice. Num Pages: 376 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 157 x 236 x 24. Weight in Grams: 664.
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.
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 ... Read more
- 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.
Show LessProduct Details
Format
Hardback
Publication date
2011
Publisher
John Wiley and Sons Ltd United Kingdom
Number of pages
374
Condition
New
Series
Statistics in Practice
Number of Pages
376
Place of Publication
New York, United States
ISBN
9780470094822
SKU
V9780470094822
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Russell B. Millar
Russell B. Millar is the author of Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB, published by Wiley.
Reviews for Maximum Likelihood Estimation and Inference
“This book is well-presented and would suit applied scientists, researchers, graduate students and particularly anyone who uses likelihood and such methods to their studies and applications.” (ISR, 2012)