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121:) 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.
187:. With the help of computational methods, he also has plots of the empirical distributions overlaid on the corresponding theoretical distributions. The computer has revolutionized simulation and has made the replication of Gosset’s experiment little more than an exercise.
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defined by an original sample of the population. It can be used to find a bootstrapped estimator of a population parameter. It can also be used to estimate the standard error of an estimator as well as to generate bootstrapped confidence intervals. The
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to perform simulations and other fundamental components in statistical analysis. One of the most well known of such devices is ERNIE, which produces random numbers that determine the winners of the
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of parameter estimates in samples under nonstandard conditions. This requires computers for practical implementations. To this point, computers have made many tedious statistical studies feasible.
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community. For the most part, the founders of the field of statistics relied on mathematics and asymptotic approximations in the development of computational statistical methodology.
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The terms 'computational statistics' and 'statistical computing' are often used interchangeably, although Carlo Lauro (a former president of the
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problems and are most useful when it is difficult to use other approaches. Monte Carlo methods are mainly used in three problem classes:
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202:. One of the first efforts to generate random digits in a fully automated way, was undertaken by the RAND Corporation in 1947. The
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Albert, J.H.; Gentle, J.E. (2004), Albert, James H; Gentle, James E (eds.), "Special
Section: Teaching Computational Statistics",
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352:. The more steps are included, the more closely the distribution of the sample matches the actual desired distribution.
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Though computational statistics is widely used today, it actually has a relatively short history of acceptance in the
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Trahan, Travis John (2019-10-03). Recent
Advances in Monte Carlo Methods at Los Alamos National Laboratory (Report).
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79:. This area is fast developing. The view that the broader concept of computing must be taught as part of general
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proportional to a known function. These samples can be used to evaluate an integral over that variable, as its
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deviates, performed methods to convert uniform deviates into other distributional forms using inverse
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976:"A History of Distribution Sampling Prior to the Era of the Computer and its Relevance to Simulation"
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Lauro, Carlo (1996), "Computational statistics or statistical computing, is that the question?",
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830:"A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data"
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Computational
Probability: Algorithms and Applications in the Mathematical Sciences
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Wilkinson, Leland (2008), "The Future of
Statistical Computing (with discussion)",
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By the mid-1950s, several articles and patents for devices had been proposed for
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The term 'Computational statistics' may also be used to refer to computationally
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Computational
Statistics: A New Agenda for Statistical Theory and Practice.
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or acceptance-rejection methods, and developed state-space methodology for
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568:& Temple Lang, D. (2010). "Computing in the Statistics Curricula",
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217:. The development of these devices were motivated from the need to use
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Later on, the scientists put forward computational ways of generating
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Statistical
Computing section of the American Statistical Association
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Modeling with Data: Tools and
Techniques for Statistical Computing
1176:, Wiley Series in Probability and Statistics, Wiley-Interscience,
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75:(or scientific computing) specific to the mathematical science of
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129:), as well as to cope with analytically intractable problems" .
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Gentle, James E.; Härdle, Wolfgang; Mori, Yuichi, eds. (2004),
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is a resampling technique used to generate samples from an
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Communications in
Statistics - Simulation and Computation
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Elements of Statistical Computing: Numerical Computation
225:, a lottery bond issued in the United Kingdom. In 1958,
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1318:
Journal of Computational & Graphical Statistics
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International Association for Statistical Computing
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505:
International Association for Statistical Computing
296:to obtain numerical results. The concept is to use
119:
International Association for Statistical Computing
1268:Data Science: Scientific and Statistical Computing
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1109:
828:Robert, Christian; Casella, George (2011-02-01).
647:Journal of Computational and Graphical Statistics
457:Journal of Statistical Computation and Simulation
450:Journal of Computational and Graphical Statistics
27:Interface between statistics and computer science
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292:is a statistical method that relies on repeated
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980:Journal of the American Statistical Association
787:Journal of the American Statistical Association
233:was developed. It is as a method to reduce the
1114:; Glen, Andrew G.; Lemis, Lawrence M. (2007),
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527:Statistical methods in artificial intelligence
265:, given some observed data. It is achieved by
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889:"History of uniform random number generation"
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599:Journal of the Washington Academy of Sciences
1313:Computational Statistics & Data Analysis
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617:Computational Statistics & Data Analysis
443:Computational Statistics & Data Analysis
63:, is the study which is the intersection of
739:: CS1 maint: numeric names: authors list (
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1230:, Springer Texts in Statistics, Springer,
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336:method creates samples from a continuous
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896:2017 Winter Simulation Conference (WSC)
781:Metropolis, Nicholas; Ulam, S. (1949).
210:, and also as a series of punch cards.
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1226:Rose, Colin; Smith, Murray D. (2002),
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602:, vol. 78, no. 4, 1988, pp. 310–322.
304:in principle. They are often used in
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1134:Elements of Computational Statistics
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370:empirical probability distribution
183:which led to the discovery of the
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300:to solve problems that might be
196:cumulative distribution function
106:, such as cases with very large
1210:Numerical Methods of Statistics
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464:Journal of Statistical Software
411:Computational materials science
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1244:Thisted, Ronald Aaron (1988),
1212:, Cambridge University Press,
1194:, Princeton University Press,
992:10.1080/01621459.1965.10480773
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690:"The probable error of a mean"
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537:List of statistical algorithms
320:, and generating draws from a
136:statistical methods including
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1604:Computational fields of study
937:"Notes on Bias in Estimation"
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251:Maximum likelihood estimation
246:Maximum likelihood estimation
206:produced were published as a
181:Monte Carlo method simulation
179:performed his now well-known
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629:10.1016/0167-9473(96)88920-1
542:List of statistical packages
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158:generalized additive models
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1266:Gharieb, Reda. R. (2017),
1089:10.1198/004017008000000460
974:Teichroew, Daniel (1965).
935:QUENOUILLE, M. H. (1956).
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50:London School of Economics
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1132:Gentle, James E. (2002),
1049:The American Statistician
953:10.1093/biomet/43.3-4.353
641:Watnik, Mitchell (2011).
571:The American Statistician
532:Free statistical software
406:Computational mathematics
396:Computational linguistics
150:kernel density estimation
90:the goal is to transform
1609:Mathematics of computing
1594:Computational statistics
1323:Statistics and Computing
1174:Computational Statistics
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904:10.1109/WSC.2017.8247790
887:Pierre L'Ecuyer (2017).
783:"The Monte Carlo Method"
485:Statistics and Computing
436:Computational Statistics
334:Markov chain Monte Carlo
328:Markov chain Monte Carlo
322:probability distribution
263:probability distribution
215:random number generators
200:Markov chain Monte Carlo
185:Student’s t-distribution
142:Markov chain Monte Carlo
98:, but the focus lies on
57:Computational statistics
33:Computational Statistics
1573:Transportation science
1208:Monahan, John (2001),
659:10.1198/jcgs.2011.204b
377:is a related technique
88:traditional statistics
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1359:Computational science
1190:Klemens, Ben (2008),
1061:10.1198/0003130042872
401:Computational physics
391:Computational biology
318:numerical integration
83:is gaining momentum.
81:statistical education
73:computational science
61:statistical computing
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30:For the journal, see
18:Statistical computing
1418:Electronic structure
1170:Hoeting, Jennifer A.
898:. pp. 202–230.
711:10.1093/biomet/6.1.1
177:William Sealy Gosset
110:and non-homogeneous
1423:Molecular mechanics
1270:, Noor Publishing,
834:Statistical Science
688:"Student" (1908).
342:probability density
271:likelihood function
104:statistical methods
1599:Numerical analysis
1380:Biological systems
719:10338.dmlcz/143545
285:Monte Carlo method
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1548:Materials science
1428:Quantum mechanics
1277:978-3-330-97256-8
1219:978-0-521-79168-7
1201:978-0-691-13314-0
1183:978-0-471-46124-1
1168:Givens, Geof H.;
1125:978-0-387-74675-3
913:978-1-5386-3428-8
857:10.1214/10-sts351
478:The Stata Journal
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208:book in 1955
189:
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1563:Engineering
1553:Mathematics
1486:Linguistics
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290:Monte Carlo
253:is used to
108:sample size
1588:Categories
1558:Statistics
1499:Lexicology
1461:Geophysics
941:Biometrika
698:Biometrika
553:References
298:randomness
267:maximizing
259:parameters
227:John Tukey
170:statistics
138:resampling
127:simulation
102:intensive
77:statistics
65:statistics
1568:Semiotics
1527:Economics
1522:Sociology
1494:Semantics
1466:Mechanics
1410:Chemistry
1385:Cognition
1069:219596225
1000:0162-1459
961:0006-3444
866:0883-4237
847:0808.2902
807:0162-1459
675:120111510
667:1061-8600
566:Nolan, D.
375:jackknife
366:bootstrap
231:jackknife
175:In 1908,
144:methods,
140:methods,
134:intensive
123:bootstrap
112:data sets
96:knowledge
35:(journal)
1517:Politics
1390:Genomics
1307:Journals
1172:(2005),
1041:Articles
815:18139350
511:See also
350:variance
306:physical
255:estimate
100:computer
92:raw data
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1375:Anatomy
1367:Biology
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241:Methods
164:History
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870:S2CID
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723:JSTOR
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