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Computational Statistics Statistics and Computing James E. Gentle Springer HB
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A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Item specifics
- Condition
- Book Title
- Computational Statistics (Statistics and Computing)
- ISBN
- 9780387981437
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
0387981438
ISBN-13
9780387981437
eBay Product ID (ePID)
73962957
Product Key Features
Number of Pages
Xxii, 728 Pages
Language
English
Publication Name
Computational Statistics
Publication Year
2009
Subject
Numerical Analysis, Probability & Statistics / General, Computer Science
Type
Textbook
Subject Area
Mathematics, Computers
Series
Statistics and Computing Ser.
Format
Hardcover
Dimensions
Item Weight
95.2 Oz
Item Length
9.3 in
Item Width
6.3 in
Additional Product Features
Intended Audience
Scholarly & Professional
Reviews
From the reviews: "This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. ... The writing in the book is quite clear and the author has done a good job providing the essence of each topic. ... Overall, I think this is an excellent book. ... This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions." (Michael J. Evans, Mathematical Reviews, Issue 2011 b) "This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. ... All terms in this work including computing terms are clearly defined." (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011) "I greatly appreciated the author's command of both numerical and statistical computing ... . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. ... I strongly recommend it for all scientific libraries." (Soubhik Chakraborty, ACM Computing Reviews, October, 2010) "This book has a very large scope in that ... it covers the dual fields of computational statistics and of statistical computing. ... must-read for all students and researchers engaging into any kind of serious statisticalprogramming. ... is well-written, in a lively and personal style. ... a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup." (Christian P. Robert, Statistical and Computation, Vol. 21, 2011), From the reviews:This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. … The writing in the book is quite clear and the author has done a good job providing the essence of each topic. … Overall, I think this is an excellent book. … This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions. (Michael J. Evans, Mathematical Reviews, Issue 2011 b)This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. … All terms in this work including computing terms are clearly defined. (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011)I greatly appreciated the author's command of both numerical and statistical computing … . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. … I strongly recommend it for all scientific libraries. (Soubhik Chakraborty, ACM Computing Reviews, October, 2010)This book has a very large scope in that … it covers the dual fields of computational statistics and of statistical computing. … must-read for all students and researchers engaging into any kind of serious statistical programming. … is well-written, in a lively and personal style. … a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup. (Christian P. Robert, Statistical and Computation, Vol. 21, 2011), From the reviews: "This is a book that covers many of the computational issues that statisticians will encounter as part of their research and applied work. ... The writing in the book is quite clear and the author has done a good job providing the essence of each topic. ... Overall, I think this is an excellent book. ... This book will give a graduate student a good overview of the field. There are exercises provided for each chapter together with some solutions." (Michael J. Evans, Mathematical Reviews, Issue 2011 b) "This book is a superior treatment of the important subject of statistical computing. I strongly recommend this book to anyone who analyzes data using either a commercial statistical software package or statistical computer programs written by the user or someone else. Thus this book is important not only for data oriented statisticians but for econometricians, psychometricians, political methodologists and biometricians as well. ... All terms in this work including computing terms are clearly defined." (Melvin Hinich, Technometrics, Vol. 53 (1), February, 2011) "I greatly appreciated the author's command of both numerical and statistical computing ... . The book also contains many exercises that substantiate the concepts, with solutions and hints in the appendix, an extensive bibliography, and a link to further literature and notes. The target readership includes undergraduates, postgraduates in statistics and allied fields such as computer science and mathematics, scientific research workers, and practitioners of statistics and numerical techniques. ... I strongly recommend it for all scientific libraries." (Soubhik Chakraborty, ACM Computing Reviews, October, 2010) "This book has a very large scope in that ... it covers the dual fields of computational statistics and of statistical computing. ... must-read for all students and researchers engaging into any kind of serious statistical programming. ... is well-written, in a lively and personal style. ... a reference book that should appear in the shortlist of any computational statistics/statistical computing graduate course as well as on the shelves of any researchers supporting his or her statistical practice with a significant dose of computing backup." (Christian P. Robert, Statistical and Computation, Vol. 21, 2011)
Dewey Edition
22
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.50285
Table Of Content
Preliminaries.- Mathematical and Statistical Preliminaries.- Statistical Computing.- Computer Storage and Arithmetic.- Algorithms and Programming.- Approximation of Functions and Numerical Quadrature.- Numerical Linear Algebra.- Solution of Nonlinear Equations and Optimization.- Generation of Random Numbers.- Methods of Computational Statistics.- Graphical Methods in Computational Statistics.- Tools for Identification of Structure in Data.- Estimation of Functions.- Monte Carlo Methods for Statistical Inference.- Data Randomization, Partitioning, and Augmentation.- Bootstrap Methods.- Exploring Data Density and Relationships.- Estimation of Probability Density Functions Using Parametric Models.- Nonparametric Estimation of Probability Density Functions.- Statistical Learning and Data Mining.- Statistical Models of Dependencies.
Synopsis
Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. The book assumes an intermediate background in mathematics, computing, and applied and theoretical statistics. The first part of the book, consisting of a single long chapter, reviews this background material while introducing computationally-intensive exploratory data analysis and computational inference. The six chapters in the second part of the book are on statistical computing. This part describes arithmetic in digital computers and how the nature of digital computations affects algorithms used in statistical methods. Building on the first chapters on numerical computations and algorithm design, the following chapters cover the main areas of statistical numerical analysis, that is, approximation of functions, numerical quadrature, numerical linear algebra, solution of nonlinear equations, optimization, and random number generation. The third and fourth parts of the book cover methods of computational statistics, including Monte Carlo methods, randomization and cross validation, the bootstrap, probability density estimation, and statistical learning. The book includes a large number of exercises with some solutions provided in an appendix., Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods., Computational inference uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally intensive statistical methods in a unified presentation.
LC Classification Number
QA273.A1-274.9
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Seller feedback (1,428)
- j***2 (1692)- Feedback left by buyer.Past monthVerified purchaseItem as described, and shipped quickly.
- 3***t (10)- Feedback left by buyer.Past monthVerified purchaseGreat seller
- _***c (27)- Feedback left by buyer.Past monthVerified purchaseOrdered a copy of “Faust” printed by the Franklin Library Trust — the book shipped fast, was packed very well, and was better than described. Looks like I time travelled and picked up a copy of a brand new book. Absolutely worth every penny! Great seller.