Numerical Analysis for Statisticians

Table of Contents

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   Book Overview
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   Chapter 1 Recurrence Relations
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   Chapter 2 Power Series Expansions
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   Chapter 3 Continued Fraction Expansions
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   Chapter 4 Asymptotic Expansions
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   Chapter 5 Solution of Nonlinear Equations
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   Chapter 6 Vector and Matrix Norms
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   Chapter 7 Linear Regression and Matrix Inversion
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   Chapter 8 Eigenvalues and Eigenvectors
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    Chapter 9 Singular Value Decomposition
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    Chapter 10 Splines
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    Chapter 11 Optimization Theory
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    Chapter 12 The MM Algorithm
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    Chapter 13 The EM Algorithm
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    Chapter 14 Newton’s Method and Scoring
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    Chapter 15 Local and Global Convergence
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    Chapter 16 Advanced Optimization Topics
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    Chapter 17 Concrete Hilbert Spaces
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    Chapter 18 Quadrature Methods
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    Chapter 19 The Fourier Transform
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    Chapter 20 The Finite Fourier Transform
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    Chapter 21 Wavelets
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    Chapter 22 Generating Random Deviates
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    Chapter 23 Independent Monte Carlo
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    Chapter 24 Permutation Tests and the Bootstrap
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    Chapter 25 Finite-State Markov Chains
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    Chapter 26 Markov Chain Monte Carlo
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    Chapter 27 Advanced Topics in MCMC