A Graduate Course on Statistical Inference (Springer Texts in Statistics)

Bing Li; G. Jogesh Babu

ISBN 10: 1493997599 / ISBN 13: 9781493997596

Published by Springer, 2019

About this Item

has checkmarks in pencil on several chapters, the text is not obscured, Shows minor wear to the cover and pages. The binding is fully intact and secure. This is a good reading or studying copy and has been verified that all pages are legible. 100% satisfaction guaranteed! --- A graduate-level treatment of statistical estimation and hypothesis testing by Bing Li and G. Jogesh Babu, published by Springer in 2019 as part of the Springer Texts in Statistics series. The volume integrates finite-sample results, asymptotic methods, and Bayesian approaches, applying a consistent framework of assumptions and analytical tools across topics. A dedicated chapter on estimating equations brings together related techniques such as generalized linear models, quasi-likelihood, generalized estimating equations, and conditional inference. The text is intended for doctoral students and researchers seeking a unified foundation in modern inferential theory.

Seller Inventory # OA-251017-shelf-20-009

Bibliographic Details

• Title: A Graduate Course on Statistical Inference (Springer Texts in Statistics)
• Publisher: Springer
• Publication Date: 2019
• Language: English
• ISBN 10: 1493997599
• ISBN 13: 9781493997596
• Binding: Hardback
• Condition: Good
• Dimensions: 12W x 12H x 4D inches 12 inches width by 12 inches height by 4 inches depth
• Weight: 4 pounds

About this title

Synopsis

This textbook offers an accessible and comprehensive overview of statistical estimation and inference that reflects current trends in statistical research. It draws from three main themes throughout: the finite-sample theory, the asymptotic theory, and Bayesian statistics. The authors have included a chapter on estimating equations as a means to unify a range of useful methodologies, including generalized linear models, generalized estimation equations, quasi-likelihood estimation, and conditional inference. They also utilize a standardized set of assumptions and tools throughout, imposing regular conditions and resulting in a more coherent and cohesive volume. Written for the graduate-level audience, this text can be used in a one-semester or two-semester course.

About the Author

Bing Li is Verne M. Wallaman Professor of Statistics at Pennsylvania State University. He is the author of Sufficient Dimension Reduction: Methods and Applications with R (2018). Dr. Li has served as an associate editor for The Annals of Statistics and is currently serving as an associate editor for Journal of the American Association.

G. Jogesh Babu is a distinguished professor of statistics, astronomy, and astrophysics, as well as director of the Center for Astrostatistics, at Pennsylvania State University. He was the 2018 winner of the Jerome Sacks Award for Cross-Disciplinary Research. He and his colleague Dr. E.D. Feigelson coined the term "astrostatistics," when they co-authored a book by the same name in 1996. Dr. Babu's numerous publications also include Statistical Challenges in Modern Astronomy V (with Feigelson, Springer 2012) and Modern Statistical Methods for Astronomy with R Applications (2012).