# Theoretical Statistics

## Topics for a Core Course

Part of the Springer Texts in Statistics book series (STS)

Advertisement

Part of the Springer Texts in Statistics book series (STS)

Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential.
The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis.
The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.
Robert Keener is Professor of Statistics at the University of Michigan and a fellow of the Institute of Mathematical Statistics.

Estimator Likelihood decision theory estimation hypothesis testing large sample theory

- DOI https://doi.org/10.1007/978-0-387-93839-4
- Copyright Information Springer Science+Business Media, LLC 2010
- Publisher Name Springer, New York, NY
- eBook Packages Mathematics and Statistics
- Print ISBN 978-0-387-93838-7
- Online ISBN 978-0-387-93839-4
- Series Print ISSN 1431-875X
- Buy this book on publisher's site