Numerical Analysis for Statisticians
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...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Livre numérique |
| Γλώσσα: | Anglais |
| Έκδοση: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Έκδοση: | 2nd ed. 2010. |
| Σειρά: | Statistics and Computing
|
| Διαθέσιμο Online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Σημείωση: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Numerical analysis for statisticians, Kenneth Lange, 2nd edition, 2010, New York, Springer, 1 vol. (XX-600 p.), Statistics and computing, 978-1-4419-5944-7 |
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| 100 | 1 | |a Lange, Kenneth, |d 1946- | |
| 245 | 1 | 0 | |a Numerical Analysis for Statisticians |c by Kenneth Lange. |
| 250 | |a 2nd ed. 2010. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Statistics and Computing |x 1431-8784 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a 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 | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. 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 | ||
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