Numerical analysis for statisticians

Kenneth Lange - Collection Statistics and Computing

360 pages, parution le 01/01/1999

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Résumé

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.

L'auteur - Kenneth Lange

UCLA School of Medicine, Los Angeles, CA, USA

Sommaire

Contents

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

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Kenneth Lange
Collection	Statistics and Computing
Parution	01/01/1999
Nb. de pages	360
Format	16 x 24
Couverture	Relié
Poids	665g
Intérieur	Noir et Blanc
EAN13	9780387949796
ISBN13	978-0-387-94979-6