Overview
- Adapts to a one-semester or two-semester graduate course in statistical inference
- Employs similar conditions throughout to unify the volume and clarify theory and methodology
- Reflects up-to-date statistical research
- Draws upon three main themes: finite-sample theory, asymptotic theory, and Bayesian statistics
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (11 chapters)
Keywords
- Bayes
- Bayesian
- Cauchy-Schwarz
- statistical inference
- statistical estimation
- finite-sample estimation
- differentiable under the integral sign
- stochastic equicontinuity
- finite-sample theory
- asymptotic theory
- posterior distributions
- empirical Bayes
- shrinkage estimates
- Le Cam-Hajek
- estimating equations
- generalized linear models
- quasi-likelihood estimation
- conditional inference
- Local Asymptotic Normal
About this book
Reviews
“This is a very nice and readable graduate level textbook of theoretical statistics. … The book is intended to be used as either a one- or a two-semester textbook of statistical inference for graduate level students, but it can also be of use to a wider group of readers interested in theoretical statistics.” (Zuzana Prášková, Mathematical Reviews, August, 2020)
Authors and Affiliations
About the authors
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).
Bibliographic Information
Book Title: A Graduate Course on Statistical Inference
Authors: Bing Li, G. Jogesh Babu
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-1-4939-9761-9
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2019
Hardcover ISBN: 978-1-4939-9759-6Published: 02 August 2019
eBook ISBN: 978-1-4939-9761-9Published: 02 August 2019
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XII, 379
Number of Illustrations: 148 b/w illustrations
Topics: Statistical Theory and Methods