Computational statistics handbook with MATLAB. (2016)

• Record Type: Book
• Further Information: Note: Wendy L. Martinez, Angel R. Martinez.
• Authors: Martinez, Wendy L; Martinez, Angel R
• 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.
• Edition: Third edition
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2016
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.5028536; Numerical analysis -- Computer programs; Mathematical statistics -- Data processing
• Languages: English
• ISBNs: 9781466592742; 9781466592766
• Related ISBNs: 9781466592735
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.