Numerical Analysis for Statisticians
(Sprache: Englisch)
This book presents topics in numerical analysis for statisticians. It would be suitable as a text for a graduate course in statistical computing. The focus is on principles of numerical analysis intended to equip students to craft their own software and to...
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This book presents topics in numerical analysis for statisticians. It would be suitable as a text for a graduate course in statistical computing. The focus is on principles of numerical analysis intended to equip students to craft their own software and to understand the advantages and disadvantages of different numerical methods.
Klappentext zu „Numerical Analysis for Statisticians “
This book presents topics in numerical analysis for statisticians. It would be suitable as a text for a graduate course in statistical computing. The focus is on principles of numerical analysis intended to equip students to craft their own software and to understand the advantages and disadvantages of different numerical methods.
Inhaltsverzeichnis zu „Numerical Analysis for Statisticians “
- 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
- Splines
- The EM Algorithm
- Newton's Method and Scoring
- Variations on the EM Theme
- Convergence of Optimization Algorithms
- Constrained Optimization
- Concrete Hilbert Spaces
- Quadrature Methods
- The Fourier Transform
- The Finite Fourier Transform
- Wavelets
- Generating Random Deviates
- Independent Monte Carlo
- Bootstrap Calculations
- Finite-State Markov Chains
- Markov Chain Monte Carlo.
Bibliographische Angaben
- Autor: Kenneth Lange
- 1999, 376 Seiten, Maße: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 0387949798
- ISBN-13: 9780387949796
Sprache:
Englisch
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