The Resource Asymptotic theory of statistics and probability, Anirban DasGupta
Asymptotic theory of statistics and probability, Anirban DasGupta
Resource Information
The item Asymptotic theory of statistics and probability, Anirban DasGupta represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 1 library branch.
Resource Information
The item Asymptotic theory of statistics and probability, Anirban DasGupta represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 1 library branch.
 Summary
 "This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics." "It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications."Jacket
 Language
 eng
 Extent
 1 online resource (xxvii, 722 pages)
 Contents

 Basic Convergence Concepts and Theorems
 Metrics, Information Theory, Convergence, and Poisson Approximations
 More General Weak and Strong Laws and the Delta Theorem
 Transformations
 More General Clts
 Moment Convergence and Uniform Integrability
 Sample Percentiles and Order Statistics
 Sample Extremes
 Central Limit theorems for Dependent Sequences
 Central Limit Theorem for Markov Chains
 Accuracy of Clts
 Invariance Principles
 Edgeworth Expansions and Cumulants
 Saddlepoint Approximations
 UStatistics
 Maximum Likelihood Estimates
 M Estimates
 the Trimmed Mean
 Multivariate Location Parameter and Multivariate Medians
 Bayes Procedures and Posterior Distributions
 Testing Problems
 Asymptotic Efficiency in Testing
 Some General Large Deviation Results
 Classical Nonparametrics
 TwoSample Problems
 Goodness of Fit
 ChiSquare Tests for Goodness of Fit
 Goodness of Fit With Estimated Parameters
 The Bootstrap
 Jackknife
 Permutation Tests
 Density Estimation
 Mixture Models and Nonparametric Deconvolution
 High Dimensional Inference and False Discovery
 Isbn
 9780387759715
 Label
 Asymptotic theory of statistics and probability
 Title
 Asymptotic theory of statistics and probability
 Statement of responsibility
 Anirban DasGupta
 Language
 eng
 Summary
 "This book is an encyclopedic treatment of classic as well as contemporary large sample theory, dealing with both statistical problems and probabilistic issues and tools. It is written in an extremely lucid style, with an emphasis on the conceptual discussion of the importance of a problem and the impact and relevance of the theorems. The book has 34 chapters over a wide range of topics, nearly 600 exercises for practice and instruction, and another 300 worked out examples. It also includes a large compendium of 300 useful inequalities on probability, linear algebra, and analysis that are collected together from numerous sources, as an invaluable reference for researchers in statistics, probability, and mathematics." "It can be used as a graduate text, as a versatile research reference, as a source for independent reading on a wide assembly of topics, and as a window to learning the latest developments in contemporary topics. The book is unique in its detailed coverage of fundamental topics such as central limit theorems in numerous setups, likelihood based methods, goodness of fit, higher order asymptotics, as well as of the most modern topics such as the bootstrap, dependent data, Bayesian asymptotics, nonparametric density estimation, mixture models, and multiple testing and false discovery. It provides extensive bibliographic references on all topics that include very recent publications."Jacket
 Cataloging source
 GW5XE
 http://library.link/vocab/creatorName
 DasGupta, Anirban
 Dewey number
 519.5
 Illustrations
 illustrations
 Index
 index present
 Language note
 English
 LC call number
 QA276
 LC item number
 .D3254 2008eb
 Literary form
 non fiction
 Nature of contents

 dictionaries
 bibliography
 Series statement
 Springer texts in statistics
 http://library.link/vocab/subjectName

 Mathematical statistics
 Mathematical statistics
 Mathematical statistics
 Label
 Asymptotic theory of statistics and probability, Anirban DasGupta
 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
 Basic Convergence Concepts and Theorems  Metrics, Information Theory, Convergence, and Poisson Approximations  More General Weak and Strong Laws and the Delta Theorem  Transformations  More General Clts  Moment Convergence and Uniform Integrability  Sample Percentiles and Order Statistics  Sample Extremes  Central Limit theorems for Dependent Sequences  Central Limit Theorem for Markov Chains  Accuracy of Clts  Invariance Principles  Edgeworth Expansions and Cumulants  Saddlepoint Approximations  UStatistics  Maximum Likelihood Estimates  M Estimates  the Trimmed Mean  Multivariate Location Parameter and Multivariate Medians  Bayes Procedures and Posterior Distributions  Testing Problems  Asymptotic Efficiency in Testing  Some General Large Deviation Results  Classical Nonparametrics  TwoSample Problems  Goodness of Fit  ChiSquare Tests for Goodness of Fit  Goodness of Fit With Estimated Parameters  The Bootstrap  Jackknife  Permutation Tests  Density Estimation  Mixture Models and Nonparametric Deconvolution  High Dimensional Inference and False Discovery
 Control code
 233972393
 Dimensions
 unknown
 Extent
 1 online resource (xxvii, 722 pages)
 Form of item
 online
 Isbn
 9780387759715
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9780387759715
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 9780387759708
 Specific material designation
 remote
 System control number
 (OCoLC)233972393
 Label
 Asymptotic theory of statistics and probability, Anirban DasGupta
 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
 Basic Convergence Concepts and Theorems  Metrics, Information Theory, Convergence, and Poisson Approximations  More General Weak and Strong Laws and the Delta Theorem  Transformations  More General Clts  Moment Convergence and Uniform Integrability  Sample Percentiles and Order Statistics  Sample Extremes  Central Limit theorems for Dependent Sequences  Central Limit Theorem for Markov Chains  Accuracy of Clts  Invariance Principles  Edgeworth Expansions and Cumulants  Saddlepoint Approximations  UStatistics  Maximum Likelihood Estimates  M Estimates  the Trimmed Mean  Multivariate Location Parameter and Multivariate Medians  Bayes Procedures and Posterior Distributions  Testing Problems  Asymptotic Efficiency in Testing  Some General Large Deviation Results  Classical Nonparametrics  TwoSample Problems  Goodness of Fit  ChiSquare Tests for Goodness of Fit  Goodness of Fit With Estimated Parameters  The Bootstrap  Jackknife  Permutation Tests  Density Estimation  Mixture Models and Nonparametric Deconvolution  High Dimensional Inference and False Discovery
 Control code
 233972393
 Dimensions
 unknown
 Extent
 1 online resource (xxvii, 722 pages)
 Form of item
 online
 Isbn
 9780387759715
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Other control number
 10.1007/9780387759715
 Other physical details
 illustrations.
 http://library.link/vocab/ext/overdrive/overdriveId
 9780387759708
 Specific material designation
 remote
 System control number
 (OCoLC)233972393
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