Computational statistics handbook with MATLAB
 Responsibility
 by Wendy L. Martinez and Angel R. Martinez.
 Edition
 Third edition.
 Publication
 Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, 2015.
 Physical description
 1 online resource (759 pages) : 240 illustrations.
 Series
 Chapman & Hall/CRCcomputer science and data analysis series.
Online
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Description
Creators/Contributors
 Author/Creator
 Martinez, Wendy L., eauthor.
 Contributor
 Martinez, Angel R., author.
 Taylor and Francis.
Contents/Summary
 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 CrossValidation Jackknife Better Bootstrap Confidence Intervals JackknifeafterBootstrap
 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 KMeans Clustering ModelBased 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 RegressionOther Details Regression Trees Additive Models Multivariate Adaptive Regression Splines
 Markov Chain Monte Carlo Methods Introduction Background MetropolisHastings Algorithms The Gibbs Sampler Convergence Monitoring
 Appendix A: MATLAB (R) Basics Appendix B: Projection Pursuit Indexes Appendix C: Data Sets Appendix D: Notation
 References
 Index
 MATLAB (R) Code, Further Reading, and Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
 Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Finding Structure. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Supervised Learning. Unsupervised Learning. Parametric Models. Nonparametric Models. Markov Chain Monte Carlo Methods. Appendices. References. Index.
 (source: Nielsen Book Data)
 Publisher's summary

A Strong Practical Focus on Applications and Algorithms Computational Statistics Handbook with MATLAB (R), 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.
(source: Nielsen Book Data)
 Publisher's summary

A Strong Practical Focus on Applications and AlgorithmsComputational 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 i.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2015
 Series
 Chapman & Hall/CRCcomputer science and data analysis series
 ISBN
 9781466592742 (ebook : PDF)