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Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB (Statistics in Practice) ハードカバー – 2011/9/9
購入オプションとあわせ買い
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.
- 本の長さ376ページ
- 言語英語
- 出版社Wiley
- 発売日2011/9/9
- 寸法15.75 x 2.39 x 23.62 cm
- ISBN-100470094826
- ISBN-13978-0470094822
商品の説明
レビュー
“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)
著者について
Russell B. Millar is the author of Maximum Likelihood Estimation and Inference: With Examples in R, SAS and ADMB, published by Wiley.
登録情報
- 出版社 : Wiley; 第1版 (2011/9/9)
- 発売日 : 2011/9/9
- 言語 : 英語
- ハードカバー : 376ページ
- ISBN-10 : 0470094826
- ISBN-13 : 978-0470094822
- 寸法 : 15.75 x 2.39 x 23.62 cm
- Amazon 売れ筋ランキング: - 46,223位洋書 (洋書の売れ筋ランキングを見る)
- カスタマーレビュー:
著者について
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他の国からのトップレビュー
This is a great reference book if you want a high-level explanation of theory and how to apply this theory to real-life examples using R, SAS, and ADMB code. It is particularly useful if you want to learn ADMB--"the best optimization software available," according to Dr. Millar.
The book, however, is a bit uneven. Some of the chapters are superb, and others are quite terse. For example, you get a good background into why Likelihood Ratio confidence intervals are superior to Wald Confidence Intervals. You also get a good introduction to Latent Variable Models with ADMB, the Exponential Family of Distributions, Generalized Linear Models, the LaPlace Approximation, and Importance Sampling. However, the examples for some of the topics (GEE, for example) are rather sparse and not especially helpful. This book is not very long, and could easily have been twice the length given the material covered.
I bought the hard-copy because it is a great reference for me, having taken Dr. Millar's course. If you want to learn ADMB, it is also recommended. But if you are not looking to learn ADMB (or TMB), you might also look at "Applied Statistical Inference: Likelihood and Bayes by Leonhard Held and Sabanés Bové, Daniel" or "Essential Statistical Inference: Theory and Methods by Dennis D. Boos and L A Stefanski." I never have found a perfect book that covers everything well on these topics. I used Millar's book in conjunction with the Applied Statistical Inference" book and found that with those two books together, I could learn what I needed. The latter book also covers Bayesian inference well and includes very helpful examples in R. (I wish Dr. Millar had included a similar section with an overview of Bayesian techniques.)
I hope Dr. Millar comes out with a second edition of this book. With a little modification, this book would be an "essential classic."