Computational Statistics Handbook with MATLAB: (Chapman & Hall/CRC Computer Science & Data Analysis)

Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with computational software. Focusing on the computational aspects of statistics rather than the theoretical, Computational Statistics Handbook with MATLAB uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors integrate the use of MATLAB throughout the book, allowing readers to see the actual implementation of algorithms, but also include step-by-step procedures to allow implementation with any suitable software. The book concentrates on the simulation/Monte Carlo point of view, and contains algorithms for exploratory data analysis, modeling, Monte Carlo simulation, pattern recognition, bootstrap, classification, cross-validation methods, probability density estimation, random number generation, and other computational statistics methods. Emphasis on the practical aspects of statistics, details of the latest techniques, and real implementation experience make the Computational Statistics Handbook with MATLAB more than just the first book to use MATLAB to solve computational problems in statistics. It also forms an outstanding, introduction to statistics for anyone in the many disciplines that involve data analysis.

Table of Contents:
PREFACE INTRODUTION What is Computational Statistics? An Overview of the Book MATLAB Code Further Reading PROBABILITY CONCEPTS Introduction Probability Conditional Probability and Independence Expectation Common Distributions MATLAB Code Further Reading Exercises SAMPLING CONCEPTS Introduction Sampling Terminology and Concepts Sampling Distributions Parameter Estimation Empirical Distribution Function MATLAB Code Further Reading Exercises GENERATING RANDOM VARIABLES Introduction General Techniques for Generating Random Variables Generating Continuous Random Variable Generating Discrete Random Variables EXPLORATORY DATA ANALYSIS Introduction Exploring Univariate Data Exploring Bivariate and Trivariate Data Exploring Multi-Dimensional Data MONTE CARLO METHODS FOR INFERENTIAL STATISTICS Introduction Classical Inferential Statistics Monte Carlo Methods for Inferential Statistics Bootstrap Methods Assessing Estimates of Functions 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 STATISTICAL PATTERN RECOGNITION Introduction Bayes Classification Evaluating the Classifier Classification Trees Clustering NONPARAMETRIC REGRESSION Introduction Smoothing Kernel Methods Regression Trees 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 APPENDICES Introduction to MATLAB Index of Notation Projection Pursuit Indexes MATLAB Code for Trees List of MATLAB Statistics Toolbox Functions List of Computational Statistics Toolbox Functions

Review :
[T]his 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.