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110:) 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.
176:. 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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191:. One of the first efforts to generate random digits in a fully automated way, was undertaken by the RAND Corporation in 1947. The
1036:
Albert, J.H.; Gentle, J.E. (2004), Albert, James H; Gentle, James E (eds.), "Special
Section: Teaching Computational Statistics",
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341:. 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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68:. 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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965:"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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819:"A Short History of Markov Chain Monte Carlo: Subjective Recollections from Incomplete Data"
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1107:, Springer International Series in Operations Research & Management Science, Springer,
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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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557:& Temple Lang, D. (2010). "Computing in the Statistics Curricula",
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206:. 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
1165:, Wiley Series in Probability and Statistics, Wiley-Interscience,
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64:(or scientific computing) specific to the mathematical science of
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118:), as well as to cope with analytically intractable problems" .
1139:
Gentle, James E.; Härdle, Wolfgang; Mori, Yuichi, eds. (2004),
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is a resampling technique used to generate samples from an
223:
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Communications in
Statistics - Simulation and Computation
1237:
Elements of Statistical Computing: Numerical Computation
214:, a lottery bond issued in the United Kingdom. In 1958,
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1307:
Journal of Computational & Graphical Statistics
1285:
International Association for Statistical Computing
875:
494:
International Association for Statistical Computing
285:to obtain numerical results. The concept is to use
108:
International Association for Statistical Computing
1257:Data Science: Scientific and Statistical Computing
1234:
1098:
817:Robert, Christian; Casella, George (2011-02-01).
636:Journal of Computational and Graphical Statistics
446:Journal of Statistical Computation and Simulation
439:Journal of Computational and Graphical Statistics
16:Interface between statistics and computer science
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281:is a statistical method that relies on repeated
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969:Journal of the American Statistical Association
776:Journal of the American Statistical Association
222:was developed. It is as a method to reduce the
1103:; Glen, Andrew G.; Lemis, Lawrence M. (2007),
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516:Statistical methods in artificial intelligence
254:, given some observed data. It is achieved by
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878:"History of uniform random number generation"
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588:Journal of the Washington Academy of Sciences
1302:Computational Statistics & Data Analysis
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606:Computational Statistics & Data Analysis
432:Computational Statistics & Data Analysis
52:, is the study which is the intersection of
728:: CS1 maint: numeric names: authors list (
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1219:, Springer Texts in Statistics, Springer,
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506:Algorithms for statistical classification
325:method creates samples from a continuous
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885:2017 Winter Simulation Conference (WSC)
770:Metropolis, Nicholas; Ulam, S. (1949).
199:, and also as a series of punch cards.
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1215:Rose, Colin; Smith, Murray D. (2002),
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591:, vol. 78, no. 4, 1988, pp. 310–322.
293:in principle. They are often used in
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1123:Elements of Computational Statistics
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266:is most probable under the assumed
33:Students working in the Statistics
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359:empirical probability distribution
172:which led to the discovery of the
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411:Computational statistics journals
632:"Early Computational Statistics"
289:to solve problems that might be
185:cumulative distribution function
95:, such as cases with very large
1199:Numerical Methods of Statistics
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917:
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453:Journal of Statistical Software
400:Computational materials science
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1233:Thisted, Ronald Aaron (1988),
1201:, Cambridge University Press,
1183:, Princeton University Press,
981:10.1080/01621459.1965.10480773
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788:10.1080/01621459.1949.10483310
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679:"The probable error of a mean"
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526:List of statistical algorithms
309:, and generating draws from a
125:statistical methods including
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1593:Computational fields of study
926:"Notes on Bias in Estimation"
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240:Maximum likelihood estimation
235:Maximum likelihood estimation
195:produced were published as a
170:Monte Carlo method simulation
168:performed his now well-known
1004:Statistical Computing with R
618:10.1016/0167-9473(96)88920-1
531:List of statistical packages
7:
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147:generalized additive models
10:
1614:
1255:Gharieb, Reda. R. (2017),
1078:10.1198/004017008000000460
963:Teichroew, Daniel (1965).
924:QUENOUILLE, M. H. (1956).
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143:artificial neural networks
39:London School of Economics
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1121:Gentle, James E. (2002),
1038:The American Statistician
942:10.1093/biomet/43.3-4.353
630:Watnik, Mitchell (2011).
560:The American Statistician
521:Free statistical software
395:Computational mathematics
385:Computational linguistics
139:kernel density estimation
79:the goal is to transform
1598:Mathematics of computing
1583:Computational statistics
1312:Statistics and Computing
1163:Computational Statistics
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893:10.1109/WSC.2017.8247790
876:Pierre L'Ecuyer (2017).
772:"The Monte Carlo Method"
474:Statistics and Computing
425:Computational Statistics
323:Markov chain Monte Carlo
317:Markov chain Monte Carlo
311:probability distribution
252:probability distribution
204:random number generators
189:Markov chain Monte Carlo
174:Student’s t-distribution
131:Markov chain Monte Carlo
87:, but the focus lies on
46:Computational statistics
22:Computational Statistics
1562:Transportation science
1197:Monahan, John (2001),
648:10.1198/jcgs.2011.204b
366:is a related technique
77:traditional statistics
42:
1348:Computational science
1179:Klemens, Ben (2008),
1050:10.1198/0003130042872
390:Computational physics
380:Computational biology
307:numerical integration
72:is gaining momentum.
70:statistical education
62:computational science
50:statistical computing
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19:For the journal, see
1407:Electronic structure
1159:Hoeting, Jennifer A.
887:. pp. 202–230.
700:10.1093/biomet/6.1.1
166:William Sealy Gosset
99:and non-homogeneous
1412:Molecular mechanics
1259:, Noor Publishing,
823:Statistical Science
677:"Student" (1908).
331:probability density
260:likelihood function
93:statistical methods
1588:Numerical analysis
1369:Biological systems
708:10338.dmlcz/143545
274:Monte Carlo method
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1537:Materials science
1417:Quantum mechanics
1266:978-3-330-97256-8
1208:978-0-521-79168-7
1190:978-0-691-13314-0
1172:978-0-471-46124-1
1157:Givens, Geof H.;
1114:978-0-387-74675-3
902:978-1-5386-3428-8
846:10.1214/10-sts351
467:The Stata Journal
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21:
1552:Engineering
1542:Mathematics
1475:Linguistics
694:(1): 1–25.
279:Monte Carlo
242:is used to
97:sample size
1577:Categories
1547:Statistics
1488:Lexicology
1450:Geophysics
930:Biometrika
687:Biometrika
542:References
287:randomness
256:maximizing
248:parameters
216:John Tukey
159:statistics
127:resampling
116:simulation
91:intensive
66:statistics
54:statistics
1557:Semiotics
1516:Economics
1511:Sociology
1483:Semantics
1455:Mechanics
1399:Chemistry
1374:Cognition
1058:219596225
989:0162-1459
950:0006-3444
855:0883-4237
836:0808.2902
796:0162-1459
664:120111510
656:1061-8600
555:Nolan, D.
364:jackknife
355:bootstrap
220:jackknife
164:In 1908,
133:methods,
129:methods,
123:intensive
112:bootstrap
101:data sets
85:knowledge
24:(journal)
1506:Politics
1379:Genomics
1296:Journals
1161:(2005),
1030:Articles
804:18139350
500:See also
339:variance
295:physical
244:estimate
89:computer
81:raw data
1532:Finance
1427:Physics
1364:Anatomy
1356:Biology
1086:3521989
911:4567651
863:2806098
757:1569710
716:2331554
329:, with
230:Methods
153:History
41:in 1964
37:of the
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1082:S2CID
1054:S2CID
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907:S2CID
881:(PDF)
859:S2CID
831:arXiv
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712:JSTOR
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