Computer intensive methods in statistics

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.