國立虎尾科技大學 |

A course in mathematical statistics and large sample theory

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A course in mathematical statistics and large sample theory/ by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru.
Author:	Bhattacharya, Rabi.
other author:	Lin, Lizhen.
Published:	New York, NY :Springer New York : : 2016.,
Description:	xi, 389 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	http://dx.doi.org/10.1007/978-1-4939-4032-5
ISBN:	9781493940325

A course in mathematical statistics and large sample theory
Bhattacharya, Rabi.

A course in mathematical statistics and large sample theory[electronic resource] /by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru. - New York, NY :Springer New York :2016. - xi, 389 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

1 Introduction -- 2 Decision Theory -- 3 Introduction to General Methods of Estimation -- 4 Sufficient Statistics, Exponential Families, and Estimation -- 5 Testing Hypotheses -- 6 Consistency and Asymptotic Distributions and Statistics -- 7 Large Sample Theory of Estimation in Parametric Models -- 8 Tests in Parametric and Nonparametric Models -- 9 The Nonparametric Bootstrap -- 10 Nonparametric Curve Estimation -- 11 Edgeworth Expansions and the Bootstrap -- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces -- 13 Multiple Testing and the False Discovery Rate -- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory -- 15 Miscellaneous Topics -- Appendices -- Solutions of Selected Exercises in Part 1.

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.

ISBN: 9781493940325

Standard No.: 10.1007/978-1-4939-4032-5doiSubjects--Topical Terms:

527941
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

A course in mathematical statistics and large sample theory
LDR:04221nam a2200325 a 4500 001 866544
003 DE-He213
005 20170217090235.0
006 m d
007 cr nn 008maaau
008 170720s2016 nyu s 0 eng d
020 $a 9781493940325 $q (electronic bk.)
020 $a 9781493940301 $q (paper)
024 7 $a 10.1007/978-1-4939-4032-5 $2 doi
035 $a 978-1-4939-4032-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .B575 2016
100 1 $a Bhattacharya, Rabi. $3 684860
245 1 2 $a A course in mathematical statistics and large sample theory $h [electronic resource] / $c by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru.
260 $a New York, NY : $b Springer New York : $b Imprint: Springer, $c 2016.
300 $a xi, 389 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a 1 Introduction -- 2 Decision Theory -- 3 Introduction to General Methods of Estimation -- 4 Sufficient Statistics, Exponential Families, and Estimation -- 5 Testing Hypotheses -- 6 Consistency and Asymptotic Distributions and Statistics -- 7 Large Sample Theory of Estimation in Parametric Models -- 8 Tests in Parametric and Nonparametric Models -- 9 The Nonparametric Bootstrap -- 10 Nonparametric Curve Estimation -- 11 Edgeworth Expansions and the Bootstrap -- 12 Frechet Means and Nonparametric Inference on Non-Euclidean Geometric Spaces -- 13 Multiple Testing and the False Discovery Rate -- 14 Markov Chain Monte Carlo (MCMC) Simulation and Bayes Theory -- 15 Miscellaneous Topics -- Appendices -- Solutions of Selected Exercises in Part 1.
520 $a 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.
650 0 $a Mathematical statistics. $3 527941
650 0 $a Sampling (Statistics) $3 527722
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Statistics for Business/Economics/Mathematical Finance/Insurance. $3 669275
650 2 4 $a Probability Theory and Stochastic Processes. $3 593945
650 2 4 $a Statistics and Computing/Statistics Programs. $3 669775
650 2 4 $a Biostatistics. $3 783654
700 1 $a Lin, Lizhen. $3 1112829
700 1 $a Patrangenaru, Victor. $3 1112830
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 595279
856 4 0 $u http://dx.doi.org/10.1007/978-1-4939-4032-5
950 $a Mathematics and Statistics (Springer-11649)