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Dedication
by Jennifer A. Hoeting, Geof H. Givens
Computational Statistics, 2nd Edition
Cover
Wiley Series in Computational Statistics
Title Page
Copyright
Dedication
Preface
Acknowledgements
Chapter 1: Review
1.1 Mathematical Notation
1.2 Taylor's Theorem and Mathematical Limit Theory
1.3 Statistical Notation and Probability Distributions
1.4 Likelihood Inference
1.5 Bayesian Inference
1.6 Statistical Limit Theory
1.7 Markov Chains
1.8 Computing
Part I: Optimization
Chapter 2: Optimization and Solving Nonlinear Equations
2.1 Univariate Problems
2.2 Multivariate Problems
Chapter 3: Combinatorial Optimization
3.1 Hard problems and NP-completeness
3.2 Local search
3.3 Simulated Annealing
3.4 Genetic algorithms
3.5 Tabu algorithms
Chapter 4: Em Optimization Methods
4.1 Missing Data, Marginalization, and Notation
4.2 The EM Algorithm
4.3 EM Variants
Part II: Integration and Simulation
Chapter 5: Numerical Integration
5.1 Newton–Côtes Quadrature
5.2 Romberg Integration
5.3 Gaussian Quadrature
5.4 Frequently Encountered Problems
Chapter 6: Simulation and Monte Carlo Integration
6.1 Introduction to the Monte Carlo Method
6.2 Exact Simulation
6.3 Approximate Simulation
6.4 Variance Reduction Techniques
Chapter 7: Markov Chain Monte Carlo
7.1 Metropolis–Hastings algorithm
7.2 Gibbs Sampling
7.3 Implementation
Chapter 8: Advanced Topics in MCMC
8.1 Adaptive MCMC
8.2 Reversible Jump MCMC
8.3 Auxiliary variable methods
8.4 Other Metropolis–Hastings Algorithms
8.5 Perfect sampling
8.6 Markov Chain Maximum Likelihood
8.7 Example: MCMC for Markov random fields
Part III: Bootstrapping
Chapter 9: Bootstrapping
9.1 The Bootstrap Principle
9.2 Basic methods
9.3 Bootstrap Inference
9.4 Reducing Monte Carlo error
9.5 Bootstrapping dependent data
9.6 Bootstrap Performance
9.7 Other uses of the bootstrap
9.8 Permutation Tests
Part IV: Density Estimation and Smoothing
Chapter 10: Nonparametric Density Estimation
10.1 Measures of Performance
10.2 Kernel Density Estimation
10.3 Nonkernel Methods
10.4 Multivariate Methods
Chapter 11: Bivariate Smoothing
11.1 Predictor–Response Data
11.2 Linear Smoothers
11.3 Comparison of Linear Smoothers
11.4 Nonlinear Smoothers
11.5 Confidence Bands
11.6 General Bivariate Data
Chapter 12: Multivariate Smoothing
12.1 Predictor–Response Data
12.2 General Multivariate Data
Data Acknowledgments
References
Index
Wiley Series in Computational Statistics
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