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Statistics and Computing Ser.: Numerical Analysis for Statisticians by Kenneth Lange (2010, Hardcover)

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Numerical Analysis for Statisticians, Hardcover by Lange, Kenneth, ISBN 1441959440, ISBN-13 9781441959447, Brand New, Free shipping in the US Presenting aspects of numerical analysis applicable to statisticians, this volume enables students to craft their own software and to understand both the advantages and challenges of numerical methods. Topics include numerical stability, accurate approximations, computational complexity and more.

About this product

Product Identifiers

PublisherSpringer New York

ISBN-101441959440

ISBN-139781441959447

eBay Product ID (ePID)92554786

Product Key Features

Number of PagesXx, 600 Pages

LanguageEnglish

Publication NameNumerical Analysis for Statisticians

Publication Year2010

SubjectMathematical & Statistical Software, Numerical Analysis, Probability & Statistics / General, Econometrics

TypeTextbook

Subject AreaMathematics, Computers, Business & Economics

AuthorKenneth Lange

SeriesStatistics and Computing Ser.

FormatHardcover

Dimensions

Item Weight81.1 Oz

Item Length9.4 in

Item Width6.3 in

Additional Product Features

Edition Number2

Intended AudienceScholarly & Professional

LCCN2010-927594

Dewey Edition21

ReviewsFrom the reviews: "This book provides reasonably good coverage of numerical methods that are important in statistical applications. ...but overall the text serves as a good introduction to computational statistics." - MATHEMATICAL REVIEWS From the reviews of the second edition: This is a comprehensive handbook for anyone with an interest in computational statistics, such as instructors, statisticians, modelers, data mining analysts, and software designers. For a reader with good working knowledge of numerical analysis, the book is useful for understanding the advantages and disadvantages of different numerical methods. â€¦ also suitable for students interested in refining their knowledge: a list of problems with gradually increasing difficulty is available, in addition to a list of very carefully chosen references (a real support for the reader). (Dragos Calitoiu, Mathematical Reviews, Issue 2011 g) The aim and scope of this edition is to provide upper level undergraduate students, graduate students and even researchers the understanding and working knowledge of different numerical methods. â€¦ The book is organized sequentially and is well structured. â€¦ The book can be served as a textbook and equally as a reference book. â€¦ the book will appeal to a broad interdisciplinary research community. It can also successfully be used as a reference book for practitioners, providing concrete examples, data and exercises of statistical applications. (Technometrics, Vol. 53 (2), May, 2011) Numerical Analysis for Statisticians is a wonderful book. It provides most of the necessary background in calculus and enough algebra to conduct rigorous numerical analyses of statistical problems. â€¦ I simply enjoyed Numerical Analysis for Statisticians from beginning until end. â€¦ Numerical Analysis for Statisticians also is recommended for more senior researchers, and not only for building one or two courses on the bases of statistical computing. â€¦ an essential book to hand to graduate students as soon as they enter a statistics program. (Christian Robert, Chance, Vol. 24 (4), 2011)Â , From the reviews:"This book provides reasonably good coverage of numerical methods that are important in statistical applications. ...but overall the text serves as a good introduction to computational statistics." - MATHEMATICAL REVIEWSFrom the reviews of the second edition:This is a comprehensive handbook for anyone with an interest in computational statistics, such as instructors, statisticians, modelers, data mining analysts, and software designers. For a reader with good working knowledge of numerical analysis, the book is useful for understanding the advantages and disadvantages of different numerical methods. â€¦ also suitable for students interested in refining their knowledge: a list of problems with gradually increasing difficulty is available, in addition to a list of very carefully chosen references (a real support for the reader). (Dragos Calitoiu, Mathematical Reviews, Issue 2011 g)Â

Number of Volumes1 vol.

IllustratedYes

Dewey Decimal519.4

Table Of ContentRecurrence Relations.- Power Series Expansions.- Continued Fraction Expansions.- Asymptotic Expansions.- Solution of Nonlinear Equations.- Vector and Matrix Norms.- Linear Regression and Matrix Inversion.- Eigenvalues and Eigenvectors.- Singular Value Decomposition.- Splines.- Optimization Theory.- The MM Algorithm.- The EM Algorithm.- Newton's Method and Scoring.- Local and Global Convergence.- Advanced Optimization Topics.- Concrete Hilbert Spaces.- Quadrature Methods.- The Fourier Transform.- The Finite Fourier Transform.- Wavelets.- Generating Random Deviates.- Independent Monte Carlo.- Permutation Tests and the Bootstrap.- Finite-State Markov Chains.- Markov Chain Monte Carlo.- Advanced Topics in MCMC.

SynopsisEvery advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and to understand the advantages and disadvantages of different numerical methods. Issues of numerical stability, accurate approximation, computational complexity, and mathematical modeling share the limelight in a broad yet rigorous overview of those parts of numerical analysis most relevant to statisticians.In this second edition, the material on optimization has been completely rewritten. There is now an entire chapter on the MM algorithm in addition to more comprehensive treatments of constrained optimization, penalty and barrier methods, and model selection via the lasso. There is also new material on the Cholesky decomposition, Gram-Schmidt orthogonalization, the QR decomposition, the singular value decomposition, and reproducing kernel Hilbert spaces. The discussions of the bootstrap, permutation testing, independent Monte Carlo, and hidden Markov chains are updated, and a new chapter on advanced MCMC topics introduces students to Markov random fields, reversible jump MCMC, and convergence analysis in Gibbssampling.Numerical Analysis for Statisticians can serve as a graduate text for a course surveying computational statistics. With a careful selection of topics and appropriate supplementation, it can be used at the undergraduate level. It contains enough material for a graduate course on optimization theory. Because many chapters are nearly self-contained, professional statisticians will also find the book useful as a reference., Numerical analysis is the study of computation and its accuracy, stability and often its implementation on a computer. This book focuses on the principles of numerical analysis and is intended to equip those readers who use statistics to craft their own software and to understand the advantages and disadvantages of different numerical methods., Presenting aspects of numerical analysis applicable to statisticians, this volume enables students to craft their own software and to understand both the advantages and challenges of numerical methods. Topics include numerical stability, accurate approximations, computational complexity and more., Every advance in computer architecture and software tempts statisticians to tackle numerically harder problems. To do so intelligently requires a good working knowledge of numerical analysis. This book equips students to craft their own software and to understand the advantages and disadvantages of different numerical methods. Issues of numerical stability, accurate approximation, computational complexity, and mathematical modeling share the limelight in a broad yet rigorous overview of those parts of numerical analysis most relevant to statisticians. In this second edition, the material on optimization has been completely rewritten. There is now an entire chapter on the MM algorithm in addition to more comprehensive treatments of constrained optimization, penalty and barrier methods, and model selection via the lasso. There is also new material on the Cholesky decomposition, Gram-Schmidt orthogonalization, the QR decomposition, the singular value decomposition, and reproducing kernel Hilbert spaces. The discussions of the bootstrap, permutation testing, independent Monte Carlo, and hidden Markov chains are updated, and a new chapter on advanced MCMC topics introduces students to Markov random fields, reversible jump MCMC, and convergence analysis in Gibbs sampling. Numerical Analysis for Statisticians can serve as a graduate text for a course surveying computational statistics. With a careful selection of topics and appropriate supplementation, it can be used at the undergraduate level. It contains enough material for a graduate course on optimization theory. Because many chapters are nearly self-contained, professional statisticians will also find the book useful as a reference.

LC Classification NumberHB139-141