As with the bestselling first edition, **Computational Statistics Handbook with MATLAB ^{®}**

**New to the Second Edition**

• New functions for multivariate normal and multivariate *t *distributions

• Updated information on the new MATLAB functionality for univariate and bivariate histograms, glyphs, and parallel coordinate plots

• New content on independent component analysis, nonlinear dimensionality reduction, and multidimensional scaling

• New topics on linear classifiers, quadratic classifiers, and voting methods, such as bagging, boosting, and random forests

• More methods for unsupervised learning, including model-based clustering and techniques for assessing the results of clustering

• A new chapter on parametric models that covers spline regression models, logistic regression, and generalized linear models

• Expanded information on smoothers, such as bin smoothing, running mean and line smoothers, and smoothing splines

With numerous problems and suggestions for further reading, this accessible text facilitates an understanding of computational statistics concepts and how they are employed in data analysis.

**Prefaces** **Introduction**

What Is Computational Statistics?

An Overview of the Book **Probability Concepts**

Introduction

Probability

Conditional Probability and Independence

Expectation

Common Distributions **Sampling Concepts**

Introduction

Sampling Terminology and Concepts

Sampling Distributions

Parameter Estimation

Empirical Distribution Function **Generating Random Variables**

Introduction

General Techniques for Generating Random Variables

Generating Continuous Random Variables

Generating Discrete Random Variables **Exploratory Data Analysis**

Introduction

Exploring Univariate Data

Exploring Bivariate and Trivariate Data

Exploring Multidimensional Data **Finding Structure**

Introduction

Projecting Data

Principal Component Analysis

Projection Pursuit EDA

Independent Component Analysis

Grand Tour

Nonlinear Dimensionality Reduction **Monte Carlo Methods for Inferential Statistics**

Introduction

Classical Inferential Statistics

Monte Carlo Methods for Inferential Statistics

Bootstrap Methods **Data Partitioning**

Introduction

Cross-Validation

Jackknife

Better Bootstrap Confidence Intervals

Jackknife-after-Bootstrap **Probability Density Estimation**

Introduction

Histograms

Kernel Density Estimation

Finite Mixtures

Generating Random Variables **Supervised Learning**

Introduction

Bayes’ Decision Theory

Evaluating the Classifier

Classification Trees

Combining Classifiers **Unsupervised Learning**

Introduction

Measures of Distance

Hierarchical Clustering

K-Means Clustering

Model-Based Clustering

Assessing Cluster Results **Parametric Models**

Introduction

Spline Regression Models

Logistic Regression

Generalized Linear Models **Nonparametric models**

Introduction

Some Smoothing Methods

Kernel Methods

Smoothing Splines

Nonparametric Regression—Other Details

Regression Trees

Additive Models **Markov Chain Monte Carlo Methods**

Introduction

Background

Metropolis–Hastings Algorithms

The Gibbs Sampler

Convergence Monitoring **Spatial Statistics**

Introduction

Visualizing Spatial Point Processes

Exploring First-Order and Second-Order Properties

Modeling Spatial Point Processes

Simulating Spatial Point Processes **Appendix A: Introduction to Matlab**

What Is MATLAB?

Getting Help in MATLAB

File and Workspace Management

Punctuation in MATLAB

Arithmetic Operators

Data Constructs in MATLAB

Script Files and Functions

Control Flow

Simple Plotting

Contact Information **Appendix B: Projection Pursuit Indexes**

Indexes

MATLAB Source Code **Appendix C: Matlab Statistics Toolbox** **Appendix D: Computational Statistics Toolbox** **Appendix E: Exploratory Data Analysis Toolboxes**

Introduction

EDA Toolbox

EDA GUI Toolbox **Appendix F: Data Sets** **Appendix G: NOTATION** **References** **INDEX** *MATLAB Code, Further Reading, and Exercises appear at the end of each chapter.*

". . . primarily useful as a reference where one can look to get a concise description of a statistical methodology and MATLAB code that can be used to implement it . . . the book is excellent."

– Michael J. Evans, in *Mathematical Reviews*, 2009e

"…My own brief assessment of the book leaves me impressed with the number of subjects covered … the book can be a valuable reference to practicing statisticians (or statistical researchers) using MATLAB as their computing engines."

—*Biometrics*, March 2009

**Praise for the First Edition **“…this book is perfectly appropriate as a textbook for an introductory course on computational statistics. It covers many useful topics, which in combination with the well-documented code, make the underlying concepts easy to grasp by the students. … Overall, this is a very nice book to be used in an undergraduate- or masters-level computational statistics course. It would also prove useful to researchers in other fields that want to learn and implement quickly some advanced statistical techniques.”

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“I am pleased to see the publication of a comprehensive book related to computational statistics and MATLAB. … this book is ambitious and well written. As a long-time user of MATLAB, I find this book useful as a reference, and thus recommend it highly to statisticians who use MATLAB. The book also would be very useful to engineers and scientists who are well trained in statistics.”

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Resource | OS Platform | Updated | Description | Instructions |
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Cross Platform | March 02, 2009 | click on http://www.pi-sigma.info/CS2E.htm |