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3rd Edition

Computational Statistics Handbook with MATLAB




ISBN 9781466592735
Published December 22, 2015 by Chapman and Hall/CRC
731 Pages 205 B/W Illustrations

 
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Book Description

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.

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Author(s)

Biography

Wendy L. Martinez is a mathematical statistician with the U.S. Bureau of Labor Statistics. She is a fellow of the American Statistical Association, a co-author of several popular Chapman & Hall/CRC books, and a MATLAB® user for more than 20 years. Her research interests include text data mining, probability density estimation, signal processing, scientific visualization, and statistical pattern recognition. She earned an M.S. in aerospace engineering from George Washington University and a Ph.D. in computational sciences and informatics from George Mason University.

Angel R. Martinez is fully retired after a long career with the U.S. federal government and as an adjunct professor at Strayer University, where he taught undergraduate and graduate courses in statistics and mathematics. Before retiring from government service, he worked for the U.S. Navy as an operations research analyst and a computer scientist. He earned an M.S. in systems engineering from the Virginia Polytechnic Institute and State University and a Ph.D. in computational sciences and informatics from George Mason University.

Reviews

Praise for Previous Editions:
"… 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, 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

"… 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. … 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."
Journal of Statistical Software, July 2004, Vol. 11

"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."
Journal of the American Statistical Association, June 2004, Vol. 99, No. 466

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