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Statistical Computing with R, Second Edtion

2nd Edition

By Maria L. Rizzo

Chapman and Hall/CRC – 2014 – 475 pages

Series: Chapman & Hall/CRC The R Series

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  • Hardback: $89.95
    978-1-46-655332-3
    November 15th 2014
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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. This second edition continues to encompass the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. It includes R code for all examples and R notes to help explain the R programming concepts. This edition also features a new chapter on nonparametric regression and smoothing.

Reviews

Praise for the First Edition:

"… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation."

—Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137

Contents

Introduction

Computational Statistics and Statistical Computing

The R Environment

Getting Started with R

Using the R Online Help System

Functions

Arrays, Data Frames, and Lists

Workspace and Files

Using Scripts

Using Packages

Graphics

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

Methods for Generating Random Variables

Introduction

The Inverse Transform Method

The Acceptance-Rejection Method

Transformation Methods

Sums and Mixtures

Multivariate Distributions

Stochastic Processes

Visualization of Multivariate Data

Introduction

Panel Displays

Surface Plots and 3D Scatter Plots

Conditioning Plots

The Grammar of Graphics and ggplot2

Other 2D Representations of Data

Projection Pursuit

Grand Tour

Other Approaches to Data Visualization

Monte Carlo Integration and Variance Reduction

Introduction

Monte Carlo Integration

Variance Reduction

Antithetic Variables

Control Variates

Importance Sampling

Stratified Sampling

Stratified Importance Sampling

Monte Carlo Methods in Inference

Introduction

Monte Carlo Methods for Estimation

Monte Carlo Methods for Hypothesis Tests

Application

Bootstrap and Jackknife

The Bootstrap

Bootstrapping Linear Models

Generalized Bootstrap

The Jackknife

Jackknife-After-Bootstrap

Bootstrap Confidence Intervals

Better Bootstrap Confidence Intervals

Application

Permutation Tests

Introduction

Tests for Equal Distributions

Multivariate Tests for Equal Distributions

Application

Markov Chain Monte Carlo Methods

Introduction

The Metropolis-Hastings Algorithm

The Gibbs Sampler

Monitoring Convergence

Application

Probability Density Estimation

Univariate Density Estimation

Kernel Density Estimation

Bivariate and Multivariate Density Estimation

Other Methods of Density Estimation

Smoothing and Nonparametric Regression

Introduction

Smoothing

Kernel Regression

High Dimensional Data

Introduction

Methods

Numerical Methods in R

Introduction

Root-Finding in One Dimension

Numerical Integration

Linear Programming—The Simplex Method

Application

Optimization

Introduction

Maximum Likelihood Problems

One-Dimensional Optimization

Two-Dimensional Optimization

The EM Algorithm

Stochastic Optimization

Exercises appear at the end of most chapters.

Name: Statistical Computing with R, Second Edtion: 2nd Edition (Hardback)Chapman and Hall/CRC 
Description: By Maria L. Rizzo. 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. This second...
Categories: Data Preparation & Mining, Statistics & Computing, Statistical Theory & Methods