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Computational Statistics with R
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Table of contents
Preface
1
Introduction
Part I: Smoothing
2
Density estimation
3
Scatterplot smoothing
4
Time series smoothing
Part II: Monte Carlo Methods
5
Random number generation
6
Rejection sampling
7
Monte Carlo integration
Part III: Optimization
8
Likelihood and optimization
9
Numerical optimization
10
Expectation maximization algorithms
11
Stochastic optimization
12
The stochastic EM algorithm
Appendix
A
R programming
References
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\(\DeclareMathOperator*{\argmin}{argmin}\) \(\DeclareMathOperator*{\argmax}{argmax}\) \(\newcommand{\E}{\mathbf{E}}\) \(\newcommand{\V}{\mathbf{Var}}\) \(\newcommand{\cov}{\mathbf{Cov}}\) \(\newcommand{\P}{\mathbf{P}}\)
12
The stochastic EM algorithm
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11
Stochastic optimization
A
R programming
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12
The stochastic EM algorithm
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