Computational Statistics Handbook with MATLAB

By Wendy L. Martinez, Ángel Martínez. 2001

816 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8c12dbd46a3ef9606526179d278d0b91dfd7c950

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In Management Science and Operations Research

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Citations in the corpus

Review and application of Artificial Neural Networks models in reliability analysis of steel structures (2015, methodology): “The basic bootstrap technique uses a sample set as a pseudo-population in order to resample and obtain statistic measures [70,71].”
State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments (2005, neutral): “For situations involving a relatively large number of factors, McKay et al. (1979) proposed LHS.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, methodology): “The books [353,352] and statistical toolbox in MATLAB [351] are helpful in implementing these statistical tests.”
POD and CVT-based reduced-order modeling of Navier-Stokes flows (2006, background): “CVTs of discrete sets are closely related to optimal k-means clusters [19,20,27,47– 49] so that Voronoi regions and centroids can be referred to as clusters and cluster centers, respectively.”
Relative Entropy Based Method for Probabilistic Sensitivity Analysis in Engineering Design (2006, background): “…obtained by the kernel density estimation (KDE) at any value of y defined as ( ) ∑ = ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − = n i i y y K n y f ˆ 1 1 δ δ (12) where K is a symmetric probability density function, called a kernel function; δ is the window width or called bandwidth; and n is the total number of samples [30].”
Classification and Regression Trees, Bagging, and Boosting (2005, background): “Martinez and Martinez (2002) provide MATLAB code for creating trees, which are similar to those that can be created by CART, although their routines do not handle nominal predictors having three or more categories or splits involving more than one variable.”
Automated Thiessen polygon generation (2006, background): “smaller quantity (e.g., 10,000 – 25,000 [ Martinez and Martinez, 2002, p. 214 ]) of combinations, there would still be a large number of Thiessen polygons to generate, therefore the execution speed of the chosen algorithm is quite important in practice, in addition to its accuracy and ease of coding”