# Statistical computing with R

##### By: Rizzo, Maria L

Material type: TextSeries: Chapman & Hall/CRC The R SeriesPublisher: Boca Raton CRC Press 2019Edition: 2ndDescription: xiv, 474 p.ISBN: 9781466553323Subject(s): Mathematical statistics - Data processing | Statistics - Data processing | R - Computer program languageDDC classification: 519.502855133 Summary: Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. https://www.crcpress.com/Statistical-Computing-with-R-Second-Edition/Rizzo/p/book/9781466553323Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library General Stacks | Slot 1423 (0 Floor, East Wing) | Non-fiction | 519.502855133 R4S8 (Browse shelf) | Checked out | 30/03/2021 | 200254 |

Table of Contents

1. Introduction

Statistical Computing

The R Environment

Getting Started with R and RStudio

Basic Syntax

Using the R Online Help System

Distributions and Statistical Tests

Functions

Arrays, Data Frames, and Lists

Formula Specifications

Graphics Introduction to ggplot

Workspace and Files

Using Scripts

Using Packages

Using R Markdown and knitr

Exercises

2. Probability and Statistics Review

Random Variables and Probability

Some Discrete Distributions

Some Continuous Distributions

Multivariate Normal Distribution

Limit Theorems

Statistics

Bayes’ Theorem and Bayesian Statistics

Markov Chains

3. Methods for Generating Random Variables

Introduction

The Inverse Transform Method

The Acceptance-Rejection Method

Transformation Methods

Sums and Mixtures

Multivariate Distributions

Exercises

4. Generating Random Processes

Stochastic Processes

Brownian Motions

Exercises

5. Visualization of Multivariate Data

Introduction

Panel Displays

Surface Plots and 3D Scatter Plots

Contour Plots

The Grammar of Graphics and ggplot2

Other 2D Representations of Data

Principal Components Analysis

Exercises

6. Monte Carlo Integration and Variance Reduction

Introduction

Monte Carlo Integration

Variance Reduction

Antithetic Variables

Control Variates

Importance Sampling

Stratified Sampling

Stratified Importance Sampling

Exercises

RCode

7. Monte Carlo Methods in Inference

Introduction

Monte Carlo Methods for Estimation

Monte Carlo Methods for Hypothesis Tests

Application

Exercises

8. Bootstrap and Jackknife

The Bootstrap

The Jackknife

Bootstrap Confidence Intervals

Better Bootstrap Confidence Intervals

Application

Exercises

9. Resampling Applications

Jackknife-after-Bootstrap

Resampling for Regression Models

Influence

Exercises

10. Permutation Tests

Introduction

Tests for Equal Distributions

Multivariate Tests for Equal Distributions

Application

Exercises

11. Markov Chain Monte Carlo Methods

Introduction

The Metropolis-Hastings Algorithm

The Gibbs Sampler

Monitoring Convergence

Application

Exercises

R Code

12. Probability Density Estimation

Univariate Density Estimation

Kernel Density Estimation

Bivariate and Multivariate Density Estimation

Other Methods of Density Estimation

Exercises

R Code

13. Introduction to Numerical Methods in R

Introduction

Root-finding in One Dimension

Numerical Integration

Maximum Likelihood Problems

Application

Exercises

14. Optimization 401

Introduction

One-dimensional Optimization

Maximum likelihood estimation with mle

Two-dimensional Optimization

The EM Algorithm

Linear Programming – The Simplex Method

Application

Exercises

15. Programming Topics

Introduction

Benchmarking: Comparing the Execution Time of Code

Profiling

Object Size, Attributes, and Equality

Finding Source Code

Linking C/C++ Code using Rcpp

Application

Exercises

Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years.

https://www.crcpress.com/Statistical-Computing-with-R-Second-Edition/Rizzo/p/book/9781466553323

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