Computer Intensive Methods in Statistics
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Computer Intensive Methods in Statistics

Computer Intensive Methods in Statistics

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About the Book

This book gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. It is written for students at Master's and PhD level.

Table of Contents:
Introduction 1. Randfom Variable Generation    Basic Methods    Congruential Generators    The KISS Generator    Beyond Uniform Distributions    Transformation Methods    Accept–Reject Methods    Envelope Accept–Reject Methods    Problems 2. Monte Carlo Methods    Independent Monte Carlo Methods    Importance Sampling    The Rule of Thumb for Importance Sampling    Markov Chain Monte Carlo - MCMC    Metropolis-Hastings Algorithm    Some Special Algorithms    Adaptive MCMC    Perfect Simulation    The Gibbs Sampler    Approximate Bayesian computation (ABC) methods    Problems 3. Bootstrap    General Principle    Unified Bootstrap Framework    Bootstrap and Monte Carlo    Conditional and Unconditional Distribution    Basic Bootstrap    Plug–in Principle    Why is Bootstrap Good?    Example, where Bootstrap Fails    Bootstrap Confidence Sets    The Pivotal Method    The Bootstrap Pivotal Methods    Percentile Bootstrap Confidence Interval    Basic Bootstrap Confidence Interval    Studentized Bootstrap Confidence Interval    Transformed Bootstrap Confidence Intervals    Prepivoting Confidence Set    BCa-Confidence Interval    Bootstrap Hypothesis Tests    Parametric Bootstrap Hypothesis Test    Nonparametric Bootstrap Hypothesis Test    Advanced Bootstrap Hypothesis Tests    Bootstrap in Regression    Model Based Bootstrap    Parametric Bootstrap Regression    Casewise Bootstrap In The Correlation Model    Bootstrap For Time Series    Problems 4. Simulation based Methods    EM - Algorithm    SIMEX    Problems 5. Density Estimation    Background    Histogram    Kernel Density Estimator    Statistical Properties    Bandwidth Selection in Practice    Nearest Neighbor Estimator    Orthogonal Series Estimators    Minimax Convergence Rates    Problems 6. Nonparametric Regression    Background    Kernel Regression Smoothing    Local Regression    Classes of Restricted Estimators    Ridge Regression    Lasso    Spline Estimators    Base Splines    Smoothing Splines    Wavelets Estimators    Wavelet Base    Wavelet Smoothing    Choosing the Smoothing Parameter    Bootstrap in Regression    Problems


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Product Details
  • ISBN-13: 9780429517808
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Routledge Cavendish
  • Language: English
  • No of Pages: 215
  • ISBN-10: 0429517807
  • Publisher Date: 13 Dec 2019
  • Binding: Digital (delivered electronically)
  • No of Pages: 215


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