"Handbook of Computational Statistics: Concepts and Methods" ed. by James E. Gentle Wolfgang Härdle, Yuichi Mori (Repost)

This volume book describes techniques used in computational statistics, and addresses some areas of application of computationally intensive methods.

The book is divided into 4 parts.
It begins with an overview of the field of Computational Statistics, how it emerged as a seperate discipline, how it developed along the development of hard- and software, including a discussion of current active research.
The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need for fast and accurate numerical algorithms, and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment.
The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data.
Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.


Table of Contents
List of Contributors
Part I. Computational Statistics
Computational Statistics: An Introduction
Part II. Statistical Computing
II.I. Basic Computational Algorithms
II.2. RandomNumber Generation
II.3. Markov Chain Monte Carlo Technology
II.4. Numerical Linear Algebra
II.5. The EMAIgorithm
II.6. Stochastic Optimization
II.7. Transforms in Statistics
II.8. Parallel Computing Techniques
II.9. Statistical Databases
II.10. Interactive and Dynamic Graphics
II.ll. The Grammar of Graphics
II.12. Statistical User Interfaces
II.13. Object Oriented Computing
Part III. Statistical Methodology
III.I. Model Selection
III.2. Bootstrap and Resampling
III.З. Design and Analysis of Monte Carlo Experiments
III.4. Multivariate DensityEstimation and Visualization
III.5. Smoothing: Local Regression Techniques
III.6. Dimension ReductionMethods
III.7. Generalized LinearModels
III.8. (Non) Linear Regression Modeling
III.9. Robust Statistics
III.10. SemiparametricModels
III.11. Bayesian IComputational Methods
III.12. ComputationalMethods in Survival Analysis
III.13. Data and KnowledgeMining
III.14. Recursive Partitioning and Tree-based Methods
III.15. Support Vector Machines
III.16. Bagging, Boosting and Ensemble Methods
Part IV. Selected Applications
IV.1. Computationally Intensive Value at Risk Calculations
IV.2. Econometrics
IV.3. Statistical and Computational IGeometry of Biomolecular Structure
IV.4. FunctionalMagnetic Resonance Imaging
IV.5. Network Intrusion Detection
Index