Essential Statistical Inference, by Dennis D. Boos
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Essential Statistical Inference, by Dennis D. Boos
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This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems.
An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology.
Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods.
Essential Statistical Inference, by Dennis D. Boos- Amazon Sales Rank: #8169319 in Books
- Published on: 2015-03-06
- Released on: 2015-03-06
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x 1.33" w x 6.10" l, 1.79 pounds
- Binding: Paperback
- 588 pages
Review
"The book is aimed at Ph.D. students in statistics who have already taken some basic graduate level Mathematical Statistics course. It succeeded in being at the perfect level to be beneficial to every statistic student. To the theoretically minded student it brings an exposure to how applications motivates statistics while to the applied student it gives theoretically motivated understanding of why the methods work. It also contains explanation of numerical methods including some implementation in R." (Jan Hannig, Journal of Agricultural, Biological, and Environmental Statistics, February, 2015)
“Throughout this well written textbook, the authors engage the reader by marrying historical descriptions of central questions in classical statistics with modern techniques and approaches. … The exercises at the end of each chapter are insightful and ideal for homework assignments. This book will surely become a widely used text for second-year graduate courses on inference, as well as an invaluable reference for statistical researchers.” (Russell T. Shinohara, The American Statistician, Vol. 68 (3), August, 2014)
“Essential statistical inference by Boos and Stefanski is an excellent book with appeal to advanced undergraduate and graduate students as well as researchers. … An appropriate list of references is given at the end of the book. … It is a welcome addition to the overcrowded statistical market and can be easily ranked as one of the best books, if not the best, on statistical inference (theory and methods).” (D. V. Chopra, Mathematical Reviews, August, 2014)
“This book is organised in five parts where the authors extensively present the roles of modelling in statistical inference (part 1), likelihood based methods (part 2), large sample approximations (part 3), methods for mis-specified likelihoods and partially defined models (part 4), and concludes with computation based methods (part 5). … The book is written in an accessible manner for both undergraduates and researchers and it is a valuable resource and starting point for statistical inference.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)
Review
"Boos and Stefanski have written a superb text that fills a void in the Mathematical Statistics genre, an area replete with texts that are either too advanced or too elementary for many statistics graduate students embarking on a research career. To the extent possible, the authors build on advanced calculus and Riemann-Stieltjes integration rather than measure theory and Lebesgue integration to define and support concepts. The authors have mindfully synthesized a wide range of fundamental statistical principles into a single volume and written in a style accessible to first- or second-year statistics graduate students. My colleagues and I have taught from this textbook or earlier iterations for the past six years and students consistently gave the text high marks for its clarity, instructive examples and end-of-chapter exercises. Instructors will also appreciate a chapter dedicated to Monte Carlo simulation studies and presenting numerical results; I have referred students to this chapter on multiple occasions. Essential Statistical Inference is an excellent reference for researchers and an outstanding instructional tool for graduate-level educators." (Brent A. Johnson, Associate Professor, Department of Biostatistics and Bioinformatics, Emory University)
"This modern treatment of graduate-level statistical inference is exceptionally well written. By thoroughly covering modern statistical topics including key computation tools in the same volume as classical material, the authors have produced the ideal textbook for a second-year inference course. The problem-motivated approach makes the book especially attractive to teach from with insightful connections highlighted between topics and across chapters. Through the marriage of historical descriptions of central questions in classical statistics with Maple and R code for examples and experiments, this text is certain to become a widely used reference book." (Taki Shinohara, Assistant Professor of Biostatistics, University of Pennsylvania)
From the Back Cover
This book is for students and researchers who have had a first year graduate level mathematical statistics course. It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems.
An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology.
Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods.
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