- Computational statistics
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Statistics algorithms were one of the first uses of modern computers.
Computational statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics.
The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the International Association for Statistical Computing) proposed making a distinction, defining 'statistical computing' as "the application of computer science to statistics", and 'computational statistics' as "aiming at the design of algorithm for implementing statistical methods on computers, including the ones unthinkable before the computer age (e.g. bootstrap, simulation), as well as to cope with analytically intractable problems" [sic].[1]
The term 'Computational statistics' may also be used to refer to computationally intensive statistical methods including resampling methods, Markov chain Monte Carlo methods, local regression, kernel density estimation, artificial neural networks and generalized additive models.
Contents
Computational statistics journals
- Communications in Statistics - Simulation and Computation
- Computational Statistics
- Computational Statistics & Data Analysis
- Journal of Computational and Graphical Statistics
- Journal of Statistical Computation and Simulation
- Journal of Statistical Software
- Statistics and Computing
- Wiley Interdisciplinary Reviews Computational Statistics
Associations
- International Association for Statistical Computing
See also
- Free statistical software
- List of statistical packages
- Machine learning
References
- ^ Lauro, Carlo (1996), "Computational statistics or statistical computing, is that the question?", Computational Statistics & Data Analysis 23: 191–193, doi:10.1016/0167-9473(96)88920-1, http://www.sciencedirect.com/science/article/B6V8V-3SWT44Y-F/2/5320a35df36fb38ffba03483c73dc861
Further reading
Articles
- Albert, J.H.; Gentle, J.E. (2004), Albert, James H; Gentle, James E, eds., "Special Section: Teaching Computational Statistics", The American Statistician 58: 1–1, doi:10.1198/0003130042872
- Wilkinson, Leland (2008), "The Future of Statistical Computing (with discussion)", Technometrics 50 (4): 418–435, doi:10.1198/004017008000000460
Books
- Drew, John H.; Evans, Diane L.; Glen, Andrew G.; Lemis, Lawrence M. (2007), Computational Probability: Algorithms and Applications in the Mathematical Sciences, Springer International Series in Operations Research & Management Science, Springer, ISBN 0387746757
- Gentle, James E. (2002), Elements of Computational Statistics, Springer, ISBN 0387954899
- Gentle, James E.; Härdle, Wolfgang; Mori, Yuichi, eds. (2004), Handbook of Computational Statistics: Concepts and Methods, Springer, ISBN 3540404643
- Givens, Geof H.; Hoeting, Jennifer A. (2005), Computational Statistics, Wiley Series in Probability and Statistics, Wiley-Interscience, ISBN 978-0471461241
- Klemens, Ben (2008), Modeling with Data: Tools and Techniques for Statistical Computing, Princeton University Press, ISBN 9780691133140
- Monahan, John (2001), Numerical Methods of Statistics, Cambridge University Press, ISBN 9780521791687
- Rose, Colin; Smith, Murray D. (2002), Mathematical Statistics with Mathematica, Springer Texts in Statistics, Springer, ISBN 0387952349
- Thisted, Ronald Aaron (1988), Elements of Statistical Computing: Numerical Computation, CRC Press, ISBN 0412013711
External links
Associations
- International Association for Statistical Computing
- Statistical Computing section of the American Statistical Association
Journals
Categories:
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