Handbook of Computational Statistics

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• Contents
  • List of Contributors
• I. Computational Statistics
  • 1. Computational Statistics: An Introduction
    • 1.1 Computational Statistics and Data Analysis
    • 1.2 The Emergence of a Field of Computational Statistics
    • 1.3 Why This Handbook
• References
• II. Statistical Computing
  • 1. Basic Computational Algorithms
    • 1.1 Computer Arithmetic
    • 1.2 Algorithms
• References
  • 2. Random Number Generation
    • 2.1 Introduction
    • 2.2 Uniform Random Number Generators
    • 2.3 Linear Recurrences Modulo
    • 2.4 Generators Based on Recurrences Modulo
    • 2.5 Nonlinear RNGs
    • 2.6 Examples of Statistical Tests
    • 2.7 Available Software and Recommendations
    • 2.8 Non-uniform Random Variate Generation
• References
  • 3. Markov Chain
    Monte Carlo Technology
    • 3.1 Introduction
    • 3.2 Markov Chains
    • 3.3 Metropolis-Hastings Algorithm
    • 3.4 The Gibbs Sampling Algorithm
    • 3.5 MCMC Sampling with Latent Variables
    • 3.6 Estimation of Density Ordinates
    • 3.7 Sampler Performance and Diagnostics
    • 3.8 Strategies for Improving Mixing
    • 3.9 Concluding Remarks
• References
  • 4. Numerical Linear Algebra
    • 4.1 Matrix Decompositions
    • 4.2 Direct Methods for Solving Linear Systems
    • 4.3 Iterative Methods for Solving Linear Systems
    • 4.4 Eigenvalues and Eigenvectors
    • 4.5 Sparse Matrices
• References
  • 5. The EM Algorithm
    • 5.1 Introduction
    • 5.2 Basic Theory of the EM Algorithm
    • 5.3 Examples of the EM Algorithm
    • 5.4 Variations on the EM Algorithm
    • 5.5 Miscellaneous Topics on the EM Algorithm
• References
  • 6. Stochastic Optimization
    • 6.1 Introduction
    • 6.2 Random Search
    • 6.3 Stochastic Approximation
    • 6.4 Genetic Algorithms
    • 6.5 Concluding Remarks
• References
  • 7. Transforms in Statistics
    • 7.1 Introduction
    • 7.2 Fourier and Related Transforms
    • 7.3 Wavelets and Other Multiscale Transforms
    • 7.4 Discrete Wavelet Transforms
    • 7.5 Conclusion
• References
  • 8. Parallel Computing Techniques
    • 8.1 Introduction
    • 8.2 Basic Ideas
    • 8.3 Parallel Computing Software
    • 8.4 Parallel Computing in Statistics
• References
  • 9. Statistical Databases
    • 9.1 Introduction
    • 9.2 Fundamentals of Data Management
    • 9.3 Architectures, Concepts and Operators
    • 9.4 Access Methods
    • 9.5 Extraction, Transformation and Loading (ETL)
    • 9.6 Metadata and XML
    • 9.7 Privacy and Security
• References
  • 10. Interactive and Dynamic Graphics
    • 10.1 Introduction
    • 10.2 Early Developments and Software
    • 10.3 Concepts of Interactive and Dynamic Graphics
    • 10.4 Graphical Software
    • 10.5 Interactive 3D Graphics
    • 10.6 Applications in Geography, Medicine, and Environmental Sciences
    • 10.7 Outlook
• References
  • 11. The Grammar of Graphics
    • 11.1 Introduction
    • 11.2 Variables
    • 11.3 Algebra
    • 11.4 Scales
    • 11.5 Statistics
    • 11.6 Geometry
    • 11.7 Coordinates
    • 11.8 Aesthetics
    • 11.9 Layout
    • 11.10 Analytics
    • 11.11 Software
    • 11.12 Conclusion
• References
  • 12. Statistical User Interfaces
    • 12.1 Introduction
    • 12.2 The Golden Rules and the ISO Norm 9241
    • 12.3 Development of Statistical User Interfaces
    • 12.4 Outlook
    • Web References
• References
  • 13. Object Oriented Computing
    • 13.1 Introduction
    • 13.2 Objects and Encapsulation
    • 13.3 Short Introduction to the UML
    • 13.4 Inheritance
    • 13.5 Polymorphism
    • 13.6 More about Inheritance
    • 13.7 Structure of the Object Oriented Program
    • 13.8 Conclusion
• References
• III. Statistical Methodology
  • 1. Model Selection
    • 1.1 Introduction
    • 1.2 Basic Concepts - Trade-Offs
    • 1.3 AIC, BIC, C and Their Variations
    • 1.4 Cross-Validation and Generalized Cross-Validation
    • 1.5 Bayes Factor
    • 1.6 Impact of Heteroscedasticity and Correlation
    • 1.7 Discussion
• References
  • 2. Bootstrap and Resampling
    • 2.1 Introduction
    • 2.2 Bootstrap as a Data Analytical Tool
    • 2.3 Resampling Tests and Confidence Intervals
    • 2.4 Bootstrap for Dependent Data
• References
  • 3. Design and Analysis of Monte Carlo Experiments
    • 3.1 Introduction
    • 3.2 Simulation Techniques in Computational Statistics
    • 3.3 Black-Box Metamodels of Simulation Models
    • 3.4 Designs for Linear Regression Models
    • 3.5 Kriging
    • 3.6 Conclusions
• References
  • 4. Multivariate Density Estimation and Visualization
    • 4.1 Introduction
    • 4.2 Visualization
    • 4.3 Density Estimation Algorithms and Theory
    • 4.4 Visualization of Trivariate Functionals
    • 4.5 Conclusions
• References
  • 5. Smoothing: Local Regression Techniques
    • 5.1 Smoothing
    • 5.2 Linear Smoothing
    • 5.3 Statistical Properties of Linear Smoothers
    • 5.4 Statistics for Linear Smoothers: Bandwidth Selection and Inference
    • 5.5 Multivariate Smoothers
• References
  • 6. Dimension Reduction Methods
    • 6.1 Introduction
    • 6.2 Linear Reduction of High-dimensional Data
    • 6.3 Nonlinear Reduction of High-dimensional Data
    • 6.4 Linear Reduction of Explanatory Variables
    • 6.5 Concluding Remarks
• References
  • 7. Generalized Linear Models
    • 7.1 Introduction
    • 7.2 Model Characteristics
    • 7.3 Estimation
    • 7.4 Practical Aspects
    • 7.5 Complements and Extensions
• References
  • 8. (Non) Linear Regression Modeling
    • 8.1 Linear Regression Modeling
    • 8.2 Nonlinear Regression Modeling
• References
  • 9. Robust Statistics
    • 9.1 Robust Statistics; Examples and Introduction
    • 9.2 Location and Scale in
    • 9.3 Location and Scale in
    • 9.4 Linear Regression
    • 9.5 Analysis of Variance
• References
  • 10. Semiparametric Models
    • 10.1 Introduction
    • 10.2 Semiparametric Models for Conditional Mean Functions
    • 10.3 The Proportional Hazards Model with Unobserved Heterogeneity
    • 10.4 A Binary Response Model
• References
  • 11. Bayesian Computational Methods
    • 11.1 Introduction
    • 11.2 Bayesian Computational Challenges
    • 11.3 Monte Carlo Methods
    • 11.4 Markov Chain Monte Carlo Methods
    • 11.5 More Monte Carlo Methods
    • 11.6 Conclusion
• References
  • 12. Computational Methods in Survival Analysis
    • 12.1 Introduction
    • 12.2 Estimation of Shape or Power Parameter
    • 12.3 Regression Models
    • 12.4 Multiple Failures and Counting Processes
• References
  • 13. Data and Knowledge Mining
    • 13.1 Data Dredging and Knowledge Discovery
    • 13.2 Knowledge Discovery in Databases
    • 13.3 Supervised and Unsupervised Learning
    • 13.4 Data Mining Tasks
    • 13.5 Data Mining Computational Methods
• References
  • 14. Recursive Partitioning and Tree-based Methods
    • 14.1 Introduction
    • 14.2 Basic Classification Trees
    • 14.3 Computational Issues
    • 14.4 Interpretation
    • 14.5 Survival Trees
    • 14.6 Tree-based Methods for Multiple Correlated Outcomes
    • 14.7 Remarks
• References
  • 15. Support Vector Machines
    • 15.1 Introduction
    • 15.2 Learning from Examples
    • 15.3 Linear SVM: Learning Theory in Practice
    • 15.4 Non-linear SVM
    • 15.5 Implementation of SVM
    • 15.6 Extensions of SVM
    • 15.7 Applications
    • 15.8 Summary and Outlook
• References
  • 16. Bagging, Boosting and Ensemble Methods
    • 16.1 An Introduction to Ensemble Methods
    • 16.2 Bagging and Related Methods
    • 16.3 Boosting
• References
• IV. Selected Applications
  • 1. Computationally Intensive Value at Risk Calculations
    • 1.1 Introduction
    • 1.2 Stable Distributions
    • 1.3 Hyperbolic Distributions
    • 1.4 Value at Risk, Portfolios and Heavy Tails
• References
  • 2. Econometrics
    • 2.1 Introduction
    • 2.2 Limited Dependent Variable Models
    • 2.3 Stochastic Volatility and Duration Models
    • 2.4 Finite Mixture Models
• References
  • 3. Statistical and Computational Geometry of Biomolecular Structure
    • 3.1 Introduction
    • 3.2 Statistical Geometry of Molecular Systems
    • 3.3 Tetrahedrality of Delaunay Simplices as a Structural Descriptor in Water
    • 3.4 Spatial and Compositional Three-dimensional Patterns in Proteins
    • 3.5 Protein Structure Comparison and Classification
    • 3.6 Conclusions
• References
  • 4. Functional Magnetic Resonance Imaging
    • 4.1 Introduction: Overview and Purpose of fMRI
    • 4.2 Background
    • 4.3 fMRI Data
    • 4.4 Modeling and Analysis
    • 4.5 Computational Issues
    • 4.6 Conclusions
• References
  • 5. Network Intrusion Detection
    • 5.1 Introduction
    • 5.2 Basic TCP/IP
    • 5.3 Passive Sensing of Denial of Service Attacks
    • 5.4 Streaming Data
    • 5.5 Visualization
    • 5.6 Profiling and Anomaly Detection
    • 5.7 Discussion
• References
• Index

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关键词：Computation Statistics statistic handbook Statist handbook Statistics

2. Bootstrap and Resampling [size=+2]Enno Mammen, Swagata Nandi


Subsections

• 2.1 Introduction
• 2.2 Bootstrap as a Data Analytical Tool
• 2.3 Resampling Tests and Confidence Intervals
• 2.4 Bootstrap for Dependent Data
  • 2.4.1 The Subsampling
  • 2.4.2 The Block Bootstrap
  • 2.4.3 The Sieve Bootstrap
  • 2.4.4 The Nonparametric Autoregressive Bootstrap
  • 2.4.5 The Regression-type Bootstrap, the Wild Bootstrap and the Local Bootstrap
  • 2.4.6 The Markov Bootstrap
  • 2.4.7 The Frequency Domain Bootstrap

Electronic Books

	Applied Multivariate Statistical Analysis	HTML
	W. H鋜dle, L. Simar, ISBN-10: 3540030794

	Applied Nonparametric Regression	HTML
	W. H鋜dle

	Applied Quantitative Finance	HTML
	W. H鋜dle, T. Kleinow, G. Stahl, ISBN 9783540434603

	Cours d'alg鑒re pour les 閠udiants candidats ?l'閏hange ENSAE-HUB	HTML
	S. Raspiller

	Cours de math閙atiques pour les 閠udiants candidats ? l'閏hange ENSAE-HUB	HTML
	T. d'Henin

	Einf黨rung in die Statistik der Finanzm鋜kte	HTML
	J. Franke, W. H鋜dle, C. Hafner, ISBN-10: 3540405585

	Handbook of Computational Statistics	HTML
	J.E. Gentle, W. H鋜dle, Y. Mori, ISBN-10: 3540404643

	Multivariate Statistics - Exercises and Solutions	HTML
	W. H鋜dle, Z. Hlavka

	Nonparametric and Semiparametric Models	HTML
	W. H鋜dle, M. M黮ler, S. Sperlich, A. Werwatz, ISBN-10: 3540207228

	Statistical Tools for Finance and Insurance	HTML
	P. Cizek, W. H鋜dle, R. Weron, ISBN-10: 3540221891

	Statistics of Financial Markets	HTML
	J. Franke, W. H鋜dle, C. Hafner, ISBN-10: 3540216758

	XploRe Applications Guide	HTML
	W. H鋜dle, Z. Hlavka, S. Klinke, ISBN-10 9783540675457

	XploRe Learning Guide	HTML
	W. H鋜dle, S. Klinke, M. M黮ler, ISBN-10: 9783540662075

last change: 2007-01-14

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Contents
List of Contributors
I. Computational Statistics
1. Computational Statistics: An Introduction
1.1 Computational Statistics and Data Analysis
1.2 The Emergence of a Field of Computational Statistics
1.3 Why This Handbook
References

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