- Part 1. Computational Statistics
- How Computational Statistics Became the Backbone of Modern Data Science / James E. Gentle, Wolfgang Karl Härdle and Yuichi Mori
- Part 2. Statistical Computing
- Basic Computational Algorithms / John F. Monahan
- Random Number Generation / Pierre L'Ecuyer
- Markov Chain Monte Carlo Technology / Siddhartha Chib
- Numerical Linear Algebra / Lenka Čížková and Pavel Čížek
- The EM Algorithm / Shu Kay Ng, Thriyambakam Krishnan and Geoffrey J. McLachlan
- Stochastic Optimization / James C. Spall
- Transforms in Statistics / Brani Vidakovic
- Parallel Computing Techniques / Junji Nakano
- Statistical Databases / Claus Boyens, Oliver Günther and Hans-J. Lenz
- Discovering and Visualizing Relations in High Dimensional Data / Alfred Inselberg
- Interactive and Dynamic Graphics / Jürgen Symanzik
- The Grammar of Graphics / Leland Wilkinson
- Statistical User Interfaces / Sigbert Klinke
- Object Oriented Computing / Miroslav Virius
- Part 3. Statistical_Methodology
- Model Selection / Yuedong Wang
- Bootstrap and Resampling / Enno Mammen and Swagata Nandi
- Design and Analysis of Monte Carlo Experiments / Jack P.C. Kleijnen
- Multivariate Density Estimation and Visualization / David W. Scott
- Smoothing: Local Regression Techniques / Catherine Loader
- Semiparametric Models / Joel L. Horowitz
- Dimension Reduction Methods / Masahiro Mizuta
- (Non) Linear Regression Modeling / Pavel Čížek
- Generalized Linear Models / Marlene Müller
- Robust Statistics / Laurie Davies and Ursula Gather
- Bayesian Computational Methods / Christian P. Robert
- Computational Methods in Survival Analysis / Toshinari Kamakura
- Data and Knowledge Mining / Adalbert Wilhelm
- Recursive Partitioning and Tree-based Methods / Heping Zhang
- Support Vector Machines / Konrad Rieck, Sören Sonnenburg, Sebastian Mika, Christin Schäfer and Pavel Laskov, et al.
- Learning Under Non-stationarity: Covariate Shift Adaptation by Importance Weighting / Masashi Sugiyama
- Saddlepoint Approximations: A Review and Some New Applications / Simon A. Broda and Marc S. Paolella
- Bagging, Boosting and Ensemble Methods / Peter Bühlmann
- Part 4. Selected Applications
- Heavy-Tailed Distributions in VaR Calculations / Adam Misiorek and Rafał Weron
- Econometrics / Luc Bauwens and Jeroen V.K. Rombouts
- Statistical and Computational Geometry of Biomolecular Structure / Iosif I. Vaisman
- Functional Magnetic Resonance Imaging / William F. Eddy and Rebecca L. McNamee
- Network Intrusion Detection / David J. Marchette.

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