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A Course in Mathematical Statistics and Large Sample Theory

  • Rabi Bhattacharya
  • Lizhen Lin
  • Victor Patrangenaru

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

Table of contents

  1. Front Matter
    Pages i-xi
  2. Mathematical Statistics: Basic (Nonasymptotic) Theory

    1. Front Matter
      Pages 1-1
    2. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 3-9
    3. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 11-17
    4. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 19-37
    5. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 39-65
    6. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 67-113
  3. Mathematical Statistics: Large Sample Theory

    1. Front Matter
      Pages 115-115
    2. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 117-164
    3. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 165-201
    4. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 203-256
    5. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 257-265
    6. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 267-278
  4. Special Topics

    1. Front Matter
      Pages 279-279
    2. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 281-302
    3. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 303-315
    4. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 317-323
    5. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 325-332
    6. Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
      Pages 333-341
  5. Back Matter
    Pages 343-389

About this book

Introduction

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.
  • 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
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. 

Keywords

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

Authors and affiliations

  • Rabi Bhattacharya
    • 1
  • Lizhen Lin
    • 2
  • Victor Patrangenaru
    • 3
  1. 1.Department of MathematicsThe University of ArizonaTucsonUSA
  2. 2.Department of Applied and Computational Mathematics and StatisticsThe University of Notre DameNotre DameUSA
  3. 3.Department of StatisticsFlorida State UniversityTallahsseeUSA

Bibliographic information

  • 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
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