Format:
Online-Ressource (XII, 1192 p. 297 illus., 96 illus. in color, digital)
Edition:
2nd ed. 2012. revised and updated
ISBN:
9783642215513
Series Statement:
Springer Handbooks of Computational Statistics
Content:
Part I Computational Statistics -- Part II Statistical Computing -- Part III Statistical Methodology -- Part IV Selected Applications.
Content:
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.
Note:
Description based upon print version of record
,
Handbook of Computational Statistics; Concepts and Methods; Contents; Contributors; Part I Computational Statistics; Chapter1 How Computational Statistics Became the Backbone of Modern Data Science; 1.1 Computational Statistics and Data Analysis; 1.2 The Emergence of a Field of Computational Statistics; 1.2.1 Early Developments in Statistical Computing; 1.2.2 Early Conferences and Formation of Learned Societies; 1.2.3 The PC; 1.2.4 The Cross Currents of Computational Statistics; 1.2.5 Reproducible Research; 1.2.6 Literature; 1.3 This Handbook; 1.3.1 Summary and Overview
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Part II: Statistical Computing1.3.2 Summary and Overview; Part III: Statistical Methodology; 1.3.3 Summary and Overview; Part IV: Statistical Methodology; 1.3.4 Other Handbooks in Computational Statistics; References; Part II Statistical Computing; Chapter2 Basic Computational Algorithms; 2.1 Computer Arithmetic; 2.1.1 Integer Arithmetic; 2.1.2 Floating Point Arithmetic; 2.1.3 Cancellation; 2.1.4 Accumulated Roundoff Error; 2.1.5 Interval Arithmetic; 2.2 Algorithms; 2.2.1 Iterative Algorithms; 2.2.2 Iterative Algorithms for Optimization and Nonlinear Equations; References
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Chapter3 Random Number Generation3.1 Introduction; 3.2 Uniform Random Number Generators; 3.2.1 Physical Devices; 3.2.2 Generators Based on a Deterministic Recurrence; 3.2.3 Quality Criteria; 3.2.4 Statistical Testing; 3.2.5 Cryptographically Strong Generators; 3.3 Linear Recurrences Modulo m; 3.3.1 The Multiple Recursive Generator; 3.3.2 The Lattice Structure; 3.3.3 MRG Implementation Techniques; 3.3.4 Combined MRGs and LCGs; 3.3.5 Jumping Ahead; 3.3.6 Linear Recurrences with Carry; 3.4 Generators Based on Recurrences Modulo 2; 3.4.1 A General Framework; 3.4.2 Measures of Uniformity
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3.4.3 Lattice Structure in Spaces of Polynomials and Formal Series3.4.4 The LFSR Generator; 3.4.5 The GFSR and Twisted GFSR; 3.4.6 Combined Linear Generators Over F2; 3.5 Nonlinear RNGs; 3.6 Empirical Statistical Tests; 3.7 Available Software and Recommendations; 3.8 Non-Uniform Random Variate Generation; 3.8.1 Inversion; 3.8.2 The Alias Method; 3.8.3 Kernel Density Estimation and Generation; 3.8.4 The Rejection Method; 3.8.5 Thinning for Point Processes with Time-Varying Rates; 3.8.6 The Ratio-of-Uniforms Method; 3.8.7 Composition and Convolution; 3.8.8 Other Special Techniques
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3.8.9 Multivariate DistributionsReferences; Chapter4 Markov Chain Monte Carlo Technology; 4.1 Introduction; 4.1.1 Organization; 4.2 Markov Chains; 4.2.1 Definitions and Results; 4.2.2 Computation of Numerical Accuracy and Inefficiency Factor; 4.3 Metropolis-Hastings Algorithm; 4.3.1 Convergence Results; 4.3.2 Example; 4.3.3 Multiple-Block M-H Algorithm; 4.4 The Gibbs Sampling Algorithm; 4.4.1 The Algorithm; 4.4.2 Invariance of the Gibbs Markov Chain; 4.4.3 Sufficient Conditions for Convergence; 4.4.4 Example: Simulating a Truncated Multivariate Normal; 4.5 MCMC Sampling with Latent Variables
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4.6 Estimation of Density Ordinates
Additional Edition:
ISBN 9783642215506
Additional Edition:
Buchausg. u.d.T. Handbook of computational statistics Berlin : Springer, 2012 ISBN 9783642215506
Language:
English
Subjects:
Computer Science
,
Economics
,
Biology
,
Mathematics
Keywords:
Statistik
;
Datenverarbeitung
;
Statistik
;
Numerisches Verfahren
;
Statistik
;
Datenverarbeitung
;
Statistik
;
Numerisches Verfahren
DOI:
10.1007/978-3-642-21551-3
URL:
Volltext
(lizenzpflichtig)
URL:
Volltext
(lizenzpflichtig)
Author information:
Härdle, Wolfgang 1953-
Author information:
Gentle, James E. 1943-
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