Cantitate/Preț
Produs

Numerical Analysis for Statisticians: Statistics and Computing

Autor Kenneth Lange
en Limba Engleză Paperback – 5 sep 2012
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 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.
Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 71042 lei  6-8 săpt.
  Springer – 5 sep 2012 71042 lei  6-8 săpt.
Hardback (1) 96615 lei  6-8 săpt.
  Springer – 15 iun 2010 96615 lei  6-8 săpt.

Din seria Statistics and Computing

Preț: 71042 lei

Preț vechi: 83579 lei
-15% Nou

Puncte Express: 1066

Preț estimativ în valută:
13595 14108$ 11363£

Carte tipărită la comandă

Livrare economică 15-29 martie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9781461426127
ISBN-10: 146142612X
Pagini: 620
Ilustrații: XX, 600 p.
Dimensiuni: 155 x 235 x 33 mm
Greutate: 0.79 kg
Ediția:Softcover reprint of hardcover 2nd ed. 2010
Editura: Springer
Colecția Springer
Seria Statistics and Computing

Locul publicării:New York, NY, United States

Public țintă

Graduate

Cuprins

Recurrence 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.

Recenzii

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 REVIEWS
From the reviews of the second edition:
“The theory and equations are well defined and easy enough to read. … This book gives you all the details you need for choosing formulas and libraries when implementing Fourier Transforms. … this is a good book … .” (Cats and Dogs with Data, maryannedata.wordpress.com, July, 2013)
“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. … The book is organized sequentially and is well structured. … The book can be served as a textbook and equally as a reference book. … 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)
“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. … 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)
“Numerical Analysis for Statisticians is a wonderful book. It provides most of the necessary background in calculusand enough algebra to conduct rigorous numerical analyses of statistical problems. … I simply enjoyed Numerical Analysis for Statisticians from beginning until end. … 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. … an essential book to hand to graduate students as soon as they enter a statistics program.” (Christian Robert, Chance, Vol. 24 (4), 2011) 

Textul de pe ultima copertă

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 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.Kenneth Lange is the Rosenfeld Professor of Computational Genetics in the Departments of Biomathematics and Human Genetics and the Chair of the Department of Human Genetics, all in the UCLA School of Medicine. His research interests include human genetics, population modeling, biomedical imaging, computational statistics, high-dimensional optimization, and applied stochastic processes. Springer previously published his books Mathematical and Statistical Methods for Genetic Analysis, 2nd ed., Applied Probability, and Optimization. He has written over 200 research papers and produced with his UCLA colleague Eric Sobel the computer program Mendel, widely used in statistical genetics.

Caracteristici

Serves as a graduate text for a survey of computational statistics Second edition adds material on optimization, MM algorithm, penalty and barrier methods, and model selection via the lasso Other major topics are updated