Book description
Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditionaTable of contents
 Preliminaries
 Preface
 Chapter 1 Introduction
 Chapter 2 Probability and Statistics Review

Chapter 3 Methods for Generating Random Variables
 3.1 Introduction
 3.2 The Inverse Transform Method
 3.3 The AcceptanceRejection Method
 3.4 Transformation Methods
 3.5 Sums and Mixtures
 3.6 Multivariate Distributions

3.7 Stochastic Processes

 Poisson Processes
 Algorithm for simulating a homogeneous Poisson process on an interval [0, t0] by generating interarrival times.
 Nonhomogeneous Poisson Processes
 Algorithm for simulating a nonhomogeneous Poisson process on an interval [0, t0] by sampling from a homogeneous Poisson process.
 Renewal Processes
 Symmetric Random Walk
 Algorithm to simulate the state Sn of a symmetric random walk
 Packages and Further Reading

 Exercises
 Chapter 4 Visualization of Multivariate Data
 Chapter 5 Monte Carlo Integration and Variance Reduction
 Chapter 6 Monte Carlo Methods in Inference
 Chapter 7 Bootstrap and Jackknife
 Chapter 8 Permutation Tests
 Chapter 9 Markov Chain Monte Carlo Methods
 Chapter 10 Probability Density Estimation
 Chapter 11 Numerical Methods in R
 Appendix A Notation
 Appendix B Working with Data Frames and Arrays
 References
Product information
 Title: Statistical Computing with R
 Author(s):
 Release date: November 2007
 Publisher(s): Chapman and Hall/CRC
 ISBN: 9781498786591
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