Authors:
- Large Sample Theory with many worked examples, numerical calculations, and simulations to illustrate theory
- Appendices provide ready access to a number of standard results, with many proofs
- Solutions given to a number of selected exercises from Part I
- Part II exercises with a certain level of difficulty appear with detailed hints
- Includes supplementary material: sn.pub/extras
Part of the book series: Springer Texts in Statistics (STS)
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Table of contents (15 chapters)
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Front Matter
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Mathematical Statistics: Basic (Nonasymptotic) Theory
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Front Matter
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Mathematical Statistics: Large Sample Theory
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Front Matter
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Back Matter
About this book
Part I of this book constitutes a one-semester course on basic parametric mathematical statistics. Part II deals with the large sample theory of statistics - parametric and nonparametric, and its contents may be covered in one semester as well. Part III provides brief accounts of a number of topics of current interest for practitioners and other disciplines whose work involves statistical methods.
Keywords
Reviews
“This is a very nice book suitable for a theoretical statistics course after having worked through something at the level of Casella & Berger, as well as some measure theory. … In addition to the exercises, which range from doable to interesting, there are several projects scattered throughout the text. The explanations are clear and crisp, and the presentation is interesting. … the book would be a worthy addition to your statistics library.” (Peter Rabinovitch, MAA Reviews, maa.org, March, 2017)
Authors and Affiliations
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Department of Mathematics, The University of Arizona, Tucson, USA
Rabi Bhattacharya
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Department of Applied and Computational Mathematics and Statistics, The University of Notre Dame, Notre Dame, USA
Lizhen Lin
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Department of Statistics, Florida State University, Tallahssee, USA
Victor Patrangenaru
About the authors
Lizhen Lin, PhD, is Assistant Professor in the Department of Statistics and Data Science at the University of Texas at Austin. She received a PhD in Mathematics from the University of Arizona and was a Postdoctoral Associate at Duke University. Bayesian nonparametrics, shape constrained inference, and nonparametric inference on manifolds are among her areas of expertise.
Vic Patrangenaru, PhD, is Professor of Statistics at Florida State University. He received PhDs in Mathematics from Haifa, Israel, and from Indiana University in the fields of differential geometry and statistics, respectively. He has many research publications on Riemannian geometry and especially on statistics on manifolds. He is a co-author with L. Ellingson of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis.
Bibliographic Information
Book Title: A Course in Mathematical Statistics and Large Sample Theory
Authors: Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-1-4939-4032-5
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 2016
Hardcover ISBN: 978-1-4939-4030-1Published: 14 August 2016
Softcover ISBN: 978-1-4939-8159-5Published: 09 June 2018
eBook ISBN: 978-1-4939-4032-5Published: 13 August 2016
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XI, 389
Number of Illustrations: 7 b/w illustrations, 2 illustrations in colour
Topics: Statistical Theory and Methods, Probability and Statistics in Computer Science, Statistics for Business, Management, Economics, Finance, Insurance, Probability Theory and Stochastic Processes, Statistics and Computing/Statistics Programs, Biostatistics