| Description |
xv, 356 pages ; 24 cm |
| Series |
Statistics and computing |
|
Statistics and computing (Springer-Verlag)
|
| Contents |
1. Recurrence Relations -- 2. Power Series Expansions -- 3. Continued Fraction Expansions -- 4. Asymptotic Expansions -- 5. Solution of Nonlinear Equations -- 6. Vector and Matrix Norms -- 7. Linear Regression and Matrix Inversion -- 8. Eigenvalues and Eigenvectors -- 9. Splines -- 10. The EM Algorithm -- 11. Newton's Method and Scoring -- 12. Variations on the EM Theme -- 13. Convergence of Optimization Algorithms -- 14. Constrained Optimization -- 15. Concrete Hilbert Spaces -- 16. Quadrature Methods -- 17. The Fourier Transform -- 18. The Finite Fourier Transform -- 19. Wavelets -- 20. Generating Random Deviates -- 21. Independent Monte Carlo -- 22. Bootstrap Calculations -- 23. Finite-State Markov Chains -- 24. Markov Chain Monte Carlo |
| Summary |
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 is intended to equip students to craft their own software and to understand the advantages and disadvantages of different numerical methods. Numerical Analysis for Statisticians can serve as a graduate text for either a one- or a two-semester course surveying computational statistics. With a careful selection of topics and appropriate supplementation, it can even be used at the undergraduate level. Because many of the chapters are nearly self-contained, professional statisticians will also find the book useful as a reference |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Mathematical statistics.
|
|
Numerical analysis.
|
| LC no. |
98016688 |
| ISBN |
0387949798 (hardcover : alk. paper) |
|
