Handbook of Computational Statistics

1. Front Matter

   Pages i-xii

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2. Computational Statistics

   1. Front Matter

      Pages 1-1

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   2. How Computational Statistics Became the Backbone of Modern Data Science

      • James E. Gentle, Wolfgang Karl Härdle, Yuichi Mori

      Pages 3-16

3. Statistical Computing

   1. Front Matter

      Pages 17-17

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   2. Basic Computational Algorithms

      • John F. Monahan

      Pages 19-33

   3. Random Number Generation

      • Pierre L’Ecuyer

      Pages 35-71

   4. Markov Chain Monte Carlo Technology

      • Siddhartha Chib

      Pages 73-104

   5. Numerical Linear Algebra

      • Lenka Čížková, Pavel Čížek

      Pages 105-137

   6. The EM Algorithm

      • Shu Kay Ng, Thriyambakam Krishnan, Geoffrey J. McLachlan

      Pages 139-172

   7. Stochastic Optimization

      • James C. Spall

      Pages 173-201

   8. Transforms in Statistics

      • Brani Vidakovic

      Pages 203-242

   9. Parallel Computing Techniques

      • Junji Nakano

      Pages 243-271

   10. Statistical Databases

       • Claus Boyens, Oliver Günther, Hans-J. Lenz

       Pages 273-297

   11. Discovering and Visualizing Relations in High Dimensional Data

       • Alfred Inselberg

       Pages 299-333

   12. Interactive and Dynamic Graphics

       • Jürgen Symanzik

       Pages 335-373

   13. The Grammar of Graphics

       • Leland Wilkinson

       Pages 375-414

   14. Statistical User Interfaces

       • Sigbert Klinke

       Pages 415-434

   15. Object Oriented Computing

       • Miroslav Virius

       Pages 435-465

4. Statistical Methodology

   1. Front Matter

      Pages 467-467

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   2. Model Selection

      • Yuedong Wang

      Pages 469-497

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.

• Bioinformatics
• Computational Statistics
• EM algorithm
• Functional MRI
• MCMC
• Network Intrusion Detection
• Randon Number Generation
• Support Vector Machines

• Dept. Computational & Data, Sciences, George Mason University, Fairfax, USA

  James E. Gentle

• L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Berlin, Germany

  Wolfgang Karl Härdle

• Dept. Socioinformation, Okayama University, Okayama, Japan

  Yuichi Mori

James E. Gentle is a Professor of Computational Statistics at George Mason University.  His research interests include Monte Carlo methods and computational finance.  He is an elected member of the ISI and a Fellow of the American Statistical Association.

Wolfgang Karl Härdle is a Professor of Statistics at the Humboldt-Universität zu Berlin and the Director of CASE – the Centre for Applied Statistics and Economics. He teaches quantitative finance and semi-parametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI and an advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.

Yuichi Mori is a Professor of Statistics and Informatics at Okayama University of Science. His research interests include efficient computing in multivariate methods, dimension reduction and variable selection, and statistics education. He is an elected member of the ISI and served as a council member of the IASC from 2003 to 2007.

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