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 Acceptance-Rejection 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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