# A Course in Mathematical Statistics and Large Sample Theory

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

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

This graduate-level textbook is primarily aimed at graduate students of statistics, mathematics, science, and engineering who have had an undergraduate course in statistics, an upper division course in analysis, and some acquaintance with measure theoretic probability. It provides a rigorous presentation of the core of mathematical statistics.

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.

**Rabi Bhattacharya, PhD,**has held regular faculty positions at UC, Berkeley; Indiana University; and the University of Arizona. He is a Fellow of the Institute of Mathematical Statistics and a recipient of the U.S. Senior Scientist Humboldt Award and of a Guggenheim Fellowship. He has served on editorial boards of many international journals and has published several research monographs and graduate texts on probability and statistics, including* Nonparametric Inference on Manifolds,* co-authored with A. Bhattacharya.

**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. *

- 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

decision theory Bayes rules Bayes estimators unbiased estimation Cramer-Rao Inequality Gauss-Markov Theorem linear models Neyman-Pearson lemma asymptotic distribution large sample theory confidence intervals bootstrap curve estimation parametric nonparametric Markov Chain Monte Carlo Simulation

- DOI https://doi.org/10.1007/978-1-4939-4032-5
- Copyright Information Springer-Verlag New York 2016
- Publisher Name Springer, New York, NY
- eBook Packages Mathematics and Statistics
- Print ISBN 978-1-4939-4030-1
- Online ISBN 978-1-4939-4032-5
- Series Print ISSN 1431-875X
- Series Online ISSN 2197-4136
- Buy this book on publisher's site