دانلود کتاب Computer Intensive Methods in Statistics

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Cover Half Title Title Page Copyright Page Contents Preface Introduction 1. Random Variable Generation 1.1 Basic Methods 1.1.1 Congruential Generators 1.1.2 The KISS Generator 1.1.3 Beyond Uniform Distributions 1.2 Transformation Methods 1.3 Accept-Reject Methods 1.3.1 Envelope Accept-Reject Methods 1.4 Problems 2. Monte Carlo Methods 2.1 Independent Monte Carlo Methods 2.1.1 Importance Sampling 2.1.2 The Rule of Thumb for Importance Sampling 2.2 Markov Chain Monte Carlo 2.2.1 Metropolis-Hastings Algorithm 2.2.2 Special MCMC Algorithms 2.2.3 Adaptive MCMC 2.2.4 Perfect Simulation 2.2.5 The Gibbs Sampler 2.3 Approximate Bayesian Computation Methods 2.4 Problems 3. Bootstrap 3.1 General Principle 3.1.1 Unified Bootstrap Framework 3.1.2 Bootstrap and Monte Carlo 3.1.3 Conditional and Unconditional Distribution 3.2 Basic Bootstrap 3.2.1 Plug-in Principle 3.2.2 Why is Bootstrap Good? 3.2.3 Example where Bootstrap Fails 3.3 Bootstrap Confidence Sets 3.3.1 The Pivotal Method 3.3.2 Bootstrap Pivotal Methods 3.3.2.1 Percentile Bootstrap Confidence Interval 3.3.2.2 Basic Bootstrap Confidence Interval 3.3.2.3 Studentized Bootstrap Confidence Interval 3.3.3 Transformed Bootstrap Confidence Intervals 3.3.4 Prepivoting Confidence Set 3.3.5 BCa-Confidence Interval 3.4 Bootstrap Hypothesis Tests 3.4.1 Parametric Bootstrap Hypothesis Test 3.4.2 Nonparametric Bootstrap Hypothesis Test 3.4.3 Advanced Bootstrap Hypothesis Tests 3.5 Bootstrap in Regression 3.5.1 Model-Based Bootstrap 3.5.2 Parametric Bootstrap Regression 3.5.3 Casewise Bootstrap in Correlation Model 3.6 Bootstrap for Time Series 3.7 Problems 4. Simulation-Based Methods 4.1 EM Algorithm 4.2 SIMEX 4.3 Variable Selection 4.3.1 F-Backward and F-Forward Procedures 4.3.2 FSR-Forward Procedure 4.3.3 SimSel 4.4 Problems 5. Density Estimation 5.1 Background 5.2 Histogram 5.3 Kernel Density Estimator 5.3.1 Statistical Properties 5.3.2 Bandwidth Selection in Practice 5.4 Nearest Neighbor Estimator 5.5 Orthogonal Series Estimator 5.6 Minimax Convergence Rate 5.7 Problems 6. Nonparametric Regression 6.1 Background 6.2 Kernel Regression Smoothing 6.3 Local Regression 6.4 Classes of Restricted Estimators 6.4.1 Ridge Regression 6.4.2 Lasso 6.5 Spline Estimators 6.5.1 Base Splines 6.5.2 Smoothing Splines 6.6 Wavelet Estimators 6.6.1 Wavelet Base 6.6.2 Wavelet Smoothing 6.7 Choosing the Smoothing Parameter 6.8 Bootstrap in Regression 6.9 Problems References Index