A Graduate Course on Statistical Inference
Published on 2. August 2019
XII, 379 pages
978-1-4939-9761-9 (ISBN)
Description
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.
Reviews / Votes
"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 Prásková, Mathematical Reviews, August, 2020)
More details
Series
Language
English
Place of publication
New York
United States
Publishing group
Springer US
Target group
Primary & secondary/elementary & high school
Product notice
Reflowable
Illustrations
XII, 379 p. 148 illus.
File size
4,95 MB
ISBN-13
978-1-4939-9761-9 (9781493997619)
DOI
10.1007/978-1-4939-9761-9
Schweitzer Classification
Other editions
Additional editions
Bing Li | G. Jogesh Babu
A Graduate Course on Statistical Inference
Book
08/2019
Springer
€171.19
Shipment within 15-20 days
Persons
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).
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).
Content
1. Probability and Random Variables.- 2. Classical Theory of Estimation.- 3. Testing Hypotheses in the Presence of Nuisance Parameters.- 4. Testing Hypotheses in the Presence of Nuisance Parameters.- 5. Basic Ideas of Bayesian Methods.- 6. Bayesian Inference.- 7. Asymptotic Tools and Projections.- 8. Asymptotic Theory for Maximum Likelihood Estimation.- 9. Estimating Equations.- 10. Convolution Theorem and Asymptotic Efficiency.- 11. Asymptotic Hypothesis Test
.
- References.- Index.
