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About this product
- Author(s)Angel R. Martinez,Wendy L. Martinez
- PublisherTaylor & Francis Inc
- GenreScience & Mathematics: Textbooks & Study Guides
- Series TitleChapman & Hall/CRC Computer Science & Data Analysis
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note205 black & white illustrations, 35 black & white tables
- Weight1280 g
- Width156 mm
- Height235 mm
- Edition Statement3rd Revised edition
- Table Of ContentsIntroduction 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(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.
- Author BiographyWendy 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(R) 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.
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