COMPUTATIONAL STATISTICS HANDBOOK WITH MATLAB (CHAPMAN & HALL/CRC By Angel R. VG
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Synopsis
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.As with the bestselling first edition, Computational Statistics Handbook with MATLAB makes computational statistics as accessible as possible by playing down theory and building an understanding of the algorithms used in a wide range of applications. This second edition recognizes the new functionality of the MATLAB statistics toolbox and other updates like the new plotting capabilities of version 7.0. It includes a discussion of GUI functionality and expanded coverage of topics such as support vector machines, bagging, boosting, and random forests in pattern recognition. The text also features updated and new problem sets, data sets, exercises, and an appendix containing answers to selected problems.Focusing on the computational aspects of statistics rather than the theoretical, this handbook uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors include algorithmic descriptions and MATLAB code for many of the latest methods in computational statistics. Detailed procedures are also included for readers who do not know MATLAB so they can implement the algorithms using other software packages. As a companion to the handbook, MATLAB functions are available for download that implement the techniques described in the text. This is the first book on the market to show how to use MATLAB to execute a wide variety of computational statistics methods and techniques.
Product Identifiers
ISBN-10
1584882298
ISBN-13
9781584882299
Key Details
Author
Angel R. Martinez, Wendy L. Martinez
Number Of Pages
616 pages
Series
Chapman and Hall/CRC Computer Science and Data Analysis
Format
ebook
Publication Date
2001-09-26
Language
English
Publisher
CRC Press LLC
Publication Year
2001
Additional Details
Copyright Date
2002
Illustrated
Yes
Dimensions
Weight
36.1 Oz
Height
1.5 In.
Width
0.6 In.
Length
0.9 In.
Target Audience
Group
Scholarly & Professional
Classification Method
LCCN
2001-042193
LC Classification Number
QA276.4.M272 2001
Dewey Decimal
519.5028537
Dewey Edition
22
Table Of Content
Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Statistical Pattern Recognition. Nonparametric Regression. Markov Chain Monte Carlo Methods. Spatial Statistics. Appendices.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
Synopsis
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.As with the bestselling first edition, Computational Statistics Handbook with MATLAB makes computational statistics as accessible as possible by playing down theory and building an understanding of the algorithms used in a wide range of applications. This second edition recognizes the new functionality of the MATLAB statistics toolbox and other updates like the new plotting capabilities of version 7.0. It includes a discussion of GUI functionality and expanded coverage of topics such as support vector machines, bagging, boosting, and random forests in pattern recognition. The text also features updated and new problem sets, data sets, exercises, and an appendix containing answers to selected problems.Focusing on the computational aspects of statistics rather than the theoretical, this handbook uses a down-to-earth approach that makes statistics accessible to a wide range of users. The authors include algorithmic descriptions and MATLAB code for many of the latest methods in computational statistics. Detailed procedures are also included for readers who do not know MATLAB so they can implement the algorithms using other software packages. As a companion to the handbook, MATLAB functions are available for download that implement the techniques described in the text. This is the first book on the market to show how to use MATLAB to execute a wide variety of computational statistics methods and techniques.
Product Identifiers
ISBN-10
1584882298
ISBN-13
9781584882299
Key Details
Author
Angel R. Martinez, Wendy L. Martinez
Number Of Pages
616 pages
Series
Chapman and Hall/CRC Computer Science and Data Analysis
Format
ebook
Publication Date
2001-09-26
Language
English
Publisher
CRC Press LLC
Publication Year
2001
Additional Details
Copyright Date
2002
Illustrated
Yes
Dimensions
Weight
36.1 Oz
Height
1.5 In.
Width
0.6 In.
Length
0.9 In.
Target Audience
Group
Scholarly & Professional
Classification Method
LCCN
2001-042193
LC Classification Number
QA276.4.M272 2001
Dewey Decimal
519.5028537
Dewey Edition
22
Table Of Content
Introduction. Probability Concepts. Sampling Concepts. Generating Random Variables. Exploratory Data Analysis. Monte Carlo Methods for Inferential Statistics. Data Partitioning. Probability Density Estimation. Statistical Pattern Recognition. Nonparametric Regression. Markov Chain Monte Carlo Methods. Spatial Statistics. Appendices.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