Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics)

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9781461448174: Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics)

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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About the Author:

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.

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)

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Dennis D. Boos, Leonard A . Stefanski
Editorial: Springer 2013-02-06 (2013)
ISBN 10: 1461448174 ISBN 13: 9781461448174
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Boos, Dennis D.
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Descripción Springer, 2013. Hardback. Estado de conservación: NEW. 9781461448174 This listing is a new book, a title currently in-print which we order directly and immediately from the publisher. Nº de ref. de la librería HTANDREE0305202

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Dennis D. Boos, Leonard A. Stefanski
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Descripción Springer-Verlag New York Inc., United States, 2013. Hardback. Estado de conservación: New. 2013 ed.. Language: English . Brand New Book. â 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. â. Nº de ref. de la librería LIB9781461448174

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Dennis D. Boos, Leonard A. Stefanski
Editorial: Springer-Verlag New York Inc., United States (2013)
ISBN 10: 1461448174 ISBN 13: 9781461448174
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Descripción Springer-Verlag New York Inc., United States, 2013. Hardback. Estado de conservación: New. 2013 ed.. Language: English . Brand New Book. â 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. â. Nº de ref. de la librería LIB9781461448174

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Boos, Dennis D.
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Descripción Springer-Verlag New York Inc., 2013. HRD. Estado de conservación: New. New Book. Delivered from our US warehouse in 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND.Established seller since 2000. Nº de ref. de la librería IP-9781461448174

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Dennis D. Boos, L A Stefanski
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Descripción Springer New York 2013-02-06, New York, 2013. hardback. Estado de conservación: New. Nº de ref. de la librería 9781461448174

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Dennis D. Boos; L A Stefanski
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Descripción Springer, 2013. Estado de conservación: New. Nº de ref. de la librería L9781461448174

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Boos, Dennis D.
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Descripción Springer, 2017. Hardcover. Estado de conservación: New. Never used! This item is printed on demand. Nº de ref. de la librería 1461448174

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