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The Resource A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource)
A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource)
Resource Information
The item A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University 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
 XI, 389 p. 9 illus., 2 illus. in color.
 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
 by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
 Subject

 Biostatistics
 Probability Theory and Stochastic Processes
 Statistical Theory and Methods
 Mathematical statistics
 Probabilities
 Statistics for Business/Economics/Mathematical Finance/Insurance
 Statistics
 Statistics and Computing/Statistics Programs
 Probabilities
 Statistics
 Probability and Statistics in Computer Science
 Electronic resources
 Mathematical statistics
 Biostatistics
 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.
 http://library.link/vocab/creatorName
 Bhattacharya, Rabi
 Image bit depth
 0
 LC call number
 QA276280
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName

 Lin, Lizhen.
 Patrangenaru, Victor.
 SpringerLink
 Series statement
 Springer Texts in Statistics,
 http://library.link/vocab/subjectName

 Statistics
 Mathematical statistics
 Biostatistics
 Probabilities
 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
 Label
 A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 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
 XI, 389 p. 9 illus., 2 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781493940325
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9781493940325
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9781493940325
 Label
 A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 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
 XI, 389 p. 9 illus., 2 illus. in color.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9781493940325
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9781493940325
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9781493940325
Subject
 Biostatistics
 Biostatistics
 Electronic resources
 Mathematical statistics
 Mathematical statistics
 Probabilities
 Probabilities
 Probability Theory and Stochastic Processes
 Probability and Statistics in Computer Science
 Statistical Theory and Methods
 Statistics
 Statistics
 Statistics and Computing/Statistics Programs
 Statistics for Business/Economics/Mathematical Finance/Insurance
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