Librería Portfolio

Features

Presents MATLAB code for various algorithms
Uses the authors' freely available Computational Statistics Toolbox for MATLAB
Introduces the basic functions of MATLAB for readers unfamiliar with the software
Incorporates theory only when it offers insight for data analysis
Supplies pseudocode so readers can implement algorithms using other software packages
Contains exercises and further reading at the end of each chapter
Provides all MATLAB code, example files, and data sets for download on the book's CRC Press web page
Figure slides are available upon qualifying course adoption.

Summary

A Strong Practical Focus on Applications and Algorithms
Computational Statistics Handbook with MATLAB®, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the implementation of the methods.

New to the Third Edition
This third edition is updated with the latest version of MATLAB and the corresponding version of the Statistics and Machine Learning Toolbox. It also incorporates new sections on the nearest neighbor classifier, support vector machines, model checking and regularization, partial least squares regression, and multivariate adaptive regression splines.

Web Resource
The authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. The MATLAB code, examples, and data sets are available online.



Table of Contents

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
Nearest Neighbor Classifier
Support Vector Machines

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
Model Selection and Regularization
Partial Least Squares Regression

Nonparametric Models
Introduction
Some Smoothing Methods
Kernel Methods
Smoothing Splines
Nonparametric Regression-Other Details
Regression Trees
Additive Models
Multivariate Adaptive Regression Splines

Markov Chain Monte Carlo Methods
Introduction
Background
Metropolis-Hastings Algorithms
The Gibbs Sampler
Convergence Monitoring

Appendix A: MATLAB® Basics
Appendix B: Projection Pursuit Indexes
Appendix C: Data Sets
Appendix D: Notation

References

Index

MATLAB® Code, Further Reading, and Exercises appear at the end of each chapter