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The Resource A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
Resource Information
The item A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 Summary
 This graduatelevel 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 onesemester 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, coauthored 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 coauthor with L. Ellingson of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
 Language
 eng
 Extent
 1 online resource (xi, 389 pages)
 Contents

 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 NonEuclidean 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
 Isbn
 9781493940325
 Label
 A course in mathematical statistics and large sample theory
 Title
 A course in mathematical statistics and large sample theory
 Statement of responsibility
 Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
 Subject

 Biostatistics
 Probability Theory and Stochastic Processes
 Statistics
 Statistical Theory and Methods
 Business & Economics  Statistics
 Sampling (Statistics)
 Electronic books
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Mathematical statistics
 Statistics and Computing/Statistics Programs
 Computers  Mathematical & Statistical Software
 Probability and Statistics in Computer Science
 Mathematical & statistical software
 Sampling (Statistics)
 Probability & statistics
 Maths for computer scientists
 Life sciences: general issues
 Mathematics  Probability & Statistics  General
 Science  Life Sciences  General
 Mathematical statistics
 Language
 eng
 Summary
 This graduatelevel 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 onesemester 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, coauthored 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 coauthor with L. Ellingson of Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
 Cataloging source
 GW5XE
 http://library.link/vocab/creatorName
 Bhattacharya, Rabi
 Dewey number
 519.5
 Illustrations
 illustrations
 Index
 index present
 LC call number
 QA276
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 http://library.link/vocab/relatedWorkOrContributorName

 Lin, Lizhen
 Patrangenaru, Victor
 Series statement
 Springer texts in statistics,
 http://library.link/vocab/subjectName

 Mathematical statistics
 Sampling (Statistics)
 Mathematical statistics
 Sampling (Statistics)
 Statistics
 Statistical Theory and Methods
 Probability and Statistics in Computer Science
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Probability Theory and Stochastic Processes
 Statistics and Computing/Statistics Programs
 Biostatistics
 Computers
 Business & Economics
 Mathematics
 Science
 Maths for computer scientists
 Probability & statistics
 Mathematical & statistical software
 Life sciences: general issues
 Label
 A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 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 NonEuclidean 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
 Dimensions
 unknown
 Extent
 1 online resource (xi, 389 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9781493940325
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Other control number
 10.1007/9781493940325
 Other physical details
 illustrations (some color).
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)956984530
 (OCoLC)ocn956984530
 Label
 A course in mathematical statistics and large sample theory, Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
 Antecedent source
 unknown
 Bibliography note
 Includes bibliographical references and index
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 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 NonEuclidean 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
 Dimensions
 unknown
 Extent
 1 online resource (xi, 389 pages)
 File format
 unknown
 Form of item
 online
 Isbn
 9781493940325
 Level of compression
 unknown
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Note
 SpringerLink
 Other control number
 10.1007/9781493940325
 Other physical details
 illustrations (some color).
 Quality assurance targets
 not applicable
 Reformatting quality
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number

 (OCoLC)956984530
 (OCoLC)ocn956984530
Subject
 Biostatistics
 Business & Economics  Statistics
 Computers  Mathematical & Statistical Software
 Electronic books
 Life sciences: general issues
 Mathematical & statistical software
 Mathematical statistics
 Mathematical statistics
 Mathematics  Probability & Statistics  General
 Maths for computer scientists
 Probability & statistics
 Probability Theory and Stochastic Processes
 Probability and Statistics in Computer Science
 Sampling (Statistics)
 Sampling (Statistics)
 Science  Life Sciences  General
 Statistical Theory and Methods
 Statistics
 Statistics and Computing/Statistics Programs
 Statistics for Business/Economics/Mathematical Finance/Insurance
Genre
Member of
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