3rd Edition

Computational Statistics Handbook with MATLAB

    760 Pages 205 B/W Illustrations
    by Chapman & Hall

    759 Pages 205 B/W Illustrations
    by Chapman & Hall

    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.

    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.

    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.

    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