Ebook: Handbook of Computational Statistics: Concepts and Methods

The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics. This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research. The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed. The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.

The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics.

This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research.

The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed.

The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.

Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.

The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics.

This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research.

The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed.

The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.

Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.

Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
How Computational Statistics Became the Backbone of Modern Data Science....Pages 3-16
Front Matter....Pages 17-17
Basic Computational Algorithms....Pages 19-33
Random Number Generation....Pages 35-71
Markov Chain Monte Carlo Technology....Pages 73-104
Numerical Linear Algebra....Pages 105-137
The EM Algorithm....Pages 139-172
Stochastic Optimization....Pages 173-201
Transforms in Statistics....Pages 203-242
Parallel Computing Techniques....Pages 243-271
Statistical Databases....Pages 273-297
Discovering and Visualizing Relations in High Dimensional Data....Pages 299-333
Interactive and Dynamic Graphics....Pages 335-373
The Grammar of Graphics....Pages 375-414
Statistical User Interfaces....Pages 415-434
Object Oriented Computing....Pages 435-465
Front Matter....Pages 467-467
Model Selection....Pages 469-497
Bootstrap and Resampling....Pages 499-527
Design and Analysis of Monte Carlo Experiments....Pages 529-547
Multivariate Density Estimation and Visualization....Pages 549-569
Smoothing: Local Regression Techniques....Pages 571-596
Front Matter....Pages 467-467
Semiparametric Models....Pages 597-617
Dimension Reduction Methods....Pages 619-644
(Non) Linear Regression Modeling....Pages 645-680
Generalized Linear Models....Pages 681-709
Robust Statistics....Pages 711-749
Bayesian Computational Methods....Pages 751-805
Computational Methods in Survival Analysis....Pages 807-824
Data and Knowledge Mining....Pages 825-852
Recursive Partitioning and Tree-based Methods....Pages 853-882
Support Vector Machines....Pages 883-926
Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting....Pages 927-952
Saddlepoint Approximations: A Review and Some New Applications....Pages 953-983
Bagging, Boosting and Ensemble Methods....Pages 985-1022
Front Matter....Pages 1023-1023
Heavy-Tailed Distributions in VaR Calculations....Pages 1025-1059
Econometrics....Pages 1061-1094
Statistical and Computational Geometry of Biomolecular Structure....Pages 1095-1112
Functional Magnetic Resonance Imaging....Pages 1113-1137
Network Intrusion Detection....Pages 1139-1165
Back Matter....Pages 1167-1192

The Handbook of Computational Statistics - Concepts and Methods (second edition) is a revision of the first edition published in 2004, and contains additional comments and updated information on the existing chapters, as well as three new chapters addressing recent work in the field of computational statistics.

This new edition is divided into 4 parts in the same way as the first edition. It begins with "How Computational Statistics became the backbone of modern data science" (Ch.1): an overview of the field of Computational Statistics, how it emerged as a separate discipline, and how its own development mirrored that of hardware and software, including a discussion of current active research.

The second part (Chs. 2 - 15) presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and some of the basic methodologies for transformation, database handling, high-dimensional data and graphics treatment are discussed.

The third part (Chs. 16 - 33) focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.

Lastly, a set of selected applications (Chs. 34 - 38) like Bioinformatics, Medical Imaging, Finance, Econometrics and Network Intrusion Detection highlight the usefulness of computational statistics in real-world applications.

Content:
Front Matter....Pages i-xii
Front Matter....Pages 1-1
How Computational Statistics Became the Backbone of Modern Data Science....Pages 3-16
Front Matter....Pages 17-17
Basic Computational Algorithms....Pages 19-33
Random Number Generation....Pages 35-71
Markov Chain Monte Carlo Technology....Pages 73-104
Numerical Linear Algebra....Pages 105-137
The EM Algorithm....Pages 139-172
Stochastic Optimization....Pages 173-201
Transforms in Statistics....Pages 203-242
Parallel Computing Techniques....Pages 243-271
Statistical Databases....Pages 273-297
Discovering and Visualizing Relations in High Dimensional Data....Pages 299-333
Interactive and Dynamic Graphics....Pages 335-373
The Grammar of Graphics....Pages 375-414
Statistical User Interfaces....Pages 415-434
Object Oriented Computing....Pages 435-465
Front Matter....Pages 467-467
Model Selection....Pages 469-497
Bootstrap and Resampling....Pages 499-527
Design and Analysis of Monte Carlo Experiments....Pages 529-547
Multivariate Density Estimation and Visualization....Pages 549-569
Smoothing: Local Regression Techniques....Pages 571-596
Front Matter....Pages 467-467
Semiparametric Models....Pages 597-617
Dimension Reduction Methods....Pages 619-644
(Non) Linear Regression Modeling....Pages 645-680
Generalized Linear Models....Pages 681-709
Robust Statistics....Pages 711-749
Bayesian Computational Methods....Pages 751-805
Computational Methods in Survival Analysis....Pages 807-824
Data and Knowledge Mining....Pages 825-852
Recursive Partitioning and Tree-based Methods....Pages 853-882
Support Vector Machines....Pages 883-926
Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting....Pages 927-952
Saddlepoint Approximations: A Review and Some New Applications....Pages 953-983
Bagging, Boosting and Ensemble Methods....Pages 985-1022
Front Matter....Pages 1023-1023
Heavy-Tailed Distributions in VaR Calculations....Pages 1025-1059
Econometrics....Pages 1061-1094
Statistical and Computational Geometry of Biomolecular Structure....Pages 1095-1112
Functional Magnetic Resonance Imaging....Pages 1113-1137
Network Intrusion Detection....Pages 1139-1165
Back Matter....Pages 1167-1192
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