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Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB 1st Edition

3 out of 5 stars 1 customer review
ISBN-13: 978-0470094822
ISBN-10: 0470094826
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Editorial Reviews

Review

“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)

 

From the Back Cover

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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Product Details

  • Hardcover: 357 pages
  • Publisher: Wiley; 1 edition (September 19, 2011)
  • Language: English
  • ISBN-10: 0470094826
  • ISBN-13: 978-0470094822
  • Product Dimensions: 6.2 x 0.9 x 9.3 inches
  • Shipping Weight: 1.5 pounds (View shipping rates and policies)
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #1,698,364 in Books (See Top 100 in Books)

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Top Customer Reviews

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I was hoping for an accessible introduction to maximum likelihood concepts. Instead, this book is a very applied text where the emphasis seems to be on describing the math and how to code it in R, SAS, and ADMB. As an example, the introduction orients the reader to maximum likelihood by providing the code for how to do a binomial problem in all three languages with just a minimal mathematical description of the procedure first. In some sense, I think this book is more appropriate for a statistician who already knows what maximum likelihood statistics is about conceptually (and definitely probability theory) and wants to learn how to put it into practice. I say this as someone who is well-versed in standard statistics and in programming but nonetheless did not find this book useful. The introduction claimed this was intended for those with just an undergraduate education in statistics but I definitely do not agree. So in the sense that it does not seem appropriate for the intended audience, I am giving it a mediocre rating, although it might be quite good for advanced readers. I'm not knowledgable enough to say.
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Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB
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