Computational Statistics Handbook with MATLAB, Second Edition

Computational Statistics Handbook with MATLAB, Second Edition

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Features

  • Provides MATLAB code for various algorithms to help readers understand important statistical concepts
  • Presents the basic functions of MATLAB, making the book accessible to all users
  • Includes MATLAB graphical interfaces for algorithms and demos, such as brushing, spinning, and grand tour
  • Supplies pseudocode so readers can implement algorithms using other software packages
  • Offers all MATLAB code, example files, and data sets available for download online
  • Summary

    As with the bestselling first edition, Computational Statistics Handbook with MATLAB®, Second Edition covers some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of the algorithms in data analysis. Updated for MATLAB® R2007a and the Statistics Toolbox, Version 6.0, this edition incorporates many additional computational statistics topics.

    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.

    Table of Contents

    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.

    Editorial Reviews

    ". . . 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.”
    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

    Downloads / Updates

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

     
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