A Course in Mathematical Statistics and Large Sample Theory

Rabi Bhattacharya

ISBN 10: 1493981595 / ISBN 13: 9781493981595

Published by Springer New York, Springer US Jun 2018, 2018

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 404 pp. Englisch.

Seller Inventory # 9781493981595

Bibliographic Details

• Title: A Course in Mathematical Statistics and Large Sample Theory
• Publisher: Springer New York, Springer US Jun 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 1493981595
• ISBN 13: 9781493981595
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 21D millimeters 254 millimeters width by 178 millimeters height by 21 millimeters depth
• Weight: 848 grams

About this title

Synopsis

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.

About the Author

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.