R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(36)
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https://qiita.com/kaizen_nagoya/items/a8eac9afbf16d2188901
What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
https://arxiv.org/abs/2012.00174
References
36
Donoho, D. L. (2017). 50 years of data science. Journal of Computational and Graphical Statistics 26, 745–766.
References on 36
36.1
Barlow, M. (2013), The Culture of Big Data, Sebastopol, CA: O’Reilly Media, Inc. [Google Scholar]
36.2
Baumer, B. (2015), “A Data Science Course for Undergraduates: Thinking With Data,” The American Statistician, 69, 334–342. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
References on 36.2
Allaire, J., Horner, J., Marti, V., and Porte, N. (2013), markdown: Markdown rendering for R, R package version 0.6.3, http://CRAN.R-project.org/package=markdown.
American Statistical Association Undergraduate Guidelines Workgroup (2014), 2014 Curricu- lum Guidelines for Undergraduate Programs in Statistical Science, http://www.amstat.org/ education/curriculumguidelines.cfm.
Anderson, C. (2008), “The End of Theory,” Wired, http://www.wired.com/science/ discoveries/magazine/16-07/pb_theory.
Bartlett, R. (2013), “We Are Data Science,” AMSTAT News, October, http://magazine.amstat. org/blog/2013/10/01/we-are-data-science/.
Box, G. E. (1979), “Some problems of statistics and everyday life,” Journal of the American Sta- tistical Association, 74, 1–4, http://www.tandfonline.com/doi/pdf/10.1080/01621459.1979. 10481600.
Breiman, L. et al. (2001), “Statistical modeling: The two cultures (with comments and a rejoinder by the author),” Statistical Science, 16, 199–231, http://www.jstor.org/stable/2676686.
Chance, B. L. (2002), “Components of Statistical Thinking and Implications for Instruction and Assessment,” Journal of Statistics Education, 10, http://www.amstat.org/publications/jse/ v10n3/chance.html.
11
Cleveland, W. S. (2001), “Data science: an action plan for expanding the technical areas of the field of statistics,” International statistical review, 69, 21–26, http://www.jstor.org/stable/1403527.
Cobb, G. W. (2007), “The Introductory Statistics Course: A Ptolemaic Curriculum?” Technology Innovations in Statistics Education (TISE), 1, http://escholarship.org/uc/item/6hb3k0nz.
— (2011), “Teaching statistics: Some important tensions,” Chilean Journal of Statistics, 2, 31–62, http://chjs.deuv.cl/Vol2N1/ChJS-02-01-03.pdf.
Cohen, D. and Henle, J. (1995), “The Pyramid Exam,” Undergraduate Mathematics Education Trends, 7, 2.
Committee on Professional Ethics (1999), Ethical Guidelines for Statistical Practice, http://www. amstat.org/about/ethicalguidelines.cfm.
Davenport, T. H. and Patil, D. (2012), “Data Scientist: The Sexiest Job of the 21st Century,” http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1.
Davidian, M. (2013a), “Aren’t We Data Science?” AMSTAT News, July, http://magazine. amstat.org/blog/2013/07/01/datascience/.
— (2013b), “The ASA and Big Data,” AMSTAT News, June, http://magazine.amstat.org/blog/ 2013/06/01/the-asa-and-big-data/.
Dhar, V. (2013), “Data Science and Prediction,” Communications of the ACM, 56, 64–73, http: //cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/fulltext.
DiGrazia, J., McKelvey, K., Bollen, J., and Rojas, F. (2013), “More Tweets, More Votes: Social Media as a Quantitative Indicator of Political Behavior,” Social Science Research Network, http: //ssrn.com/abstract=2235423.
Finzer, W. (2013), “The Data Science Education Dilemma,” Technology Innovations in Statistics Education, 7, http://escholarship.org/uc/item/7gv0q9dc.pdf.
Franck, C. (2013), “Is Nate Silver a Statistician?” AMSTAT News, October, http://magazine. amstat.org/blog/2013/10/01/is-nate-silver/.
Gelman, A. (2013), “The Tweets-Votes Curve,” http://andrewgelman.com/2013/04/24/ the-tweets-votes-curve/.
Gould, R., Baumer, B., C ̧etinkaya Rundel, M., and Bray, A. (2014), “Big Data Goes to College,” AMSTAT News, June, http://magazine.amstat.org/blog/2014/06/01/datafest/.
Halvorsen, K. T. and Moore, T. L. (2001), “Motivating, monitoring, and evaluating student projects,” MAA Notes, 27–32.
Harris, J. G., Shetterley, N., Alter, A. E., and Schnell, K. (2014), “It Takes Teams to Solve the Data Scientist Shortage,” The Wall Street Journal, http://blogs.wsj.com/cio/2014/02/14/ it-takes-teams-to-solve-the-data-scientist-shortage/.
Hart, M. and Newby, G. (2013), “Project Gutenberg,” http://www.gutenberg.org/wiki/Main_ Page.
Horton, N. J. (2015), “Challenges and opportunities for statistics and statistical education: looking back, looking forward,” arXiv preprint arXiv:1503.02188.
Horton, N. J., Baumer, B. S., and Wickham, H. (2015), “Setting the stage for data science: integra- tion of data management skills in introductory and second courses in statistics,” arXiv preprint arXiv:1502.00318.
12
IMDB.com (2013), “Internet Movie Database,” http://www.imdb.com/help/show_article? conditions.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An introduction to statistical learning, Springer, http://www-bcf.usc.edu/~gareth/ISL/.
Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N. H., Weaver, C., Lee, B., Brodbeck, D., and Buono, P. (2011), “Research directions in data wrangling: Visualizations and transformations for usable and credible data,” Information Visualization, 10, 271–288, http: //research.microsoft.com/EN-US/UM/REDMOND/GROUPS/cue/infovis/.
Linkins, J. (2013), “Let’s Calm Down About Twitter Being Able To Predict Elections, Guys,” http: //www.huffingtonpost.com/2013/08/14/twitter-predict-elections_n_3755326.html.
Lohr, S. (2009), “For Today’s Graduate, Just One Word: Statistics,” http://www.nytimes.com/ 2009/08/06/technology/06stats.html.
Moore, D. S. (1998), “Statistics among the liberal arts,” Journal of the American Statistical Asso- ciation, 93, 1253–1259, http://www.jstor.org/stable/2670040.
Mosteller, F. and Wallace, D. L. (1963), “Inference in an authorship problem: A comparative study of discrimination methods applied to the authorship of the disputed Federalist Papers,” Journal of the American Statistical Association, 58, 275–309.
Murrell, P. (2010), Introduction to Data Technologies, Chapman and Hall/CRC, https://www. stat.auckland.ac.nz/~paul/ItDT/.
Nolan, D. and Temple Lang, D. (2010), “Computing in the statistics curricula,” The Amer- ican Statistician, 64, 97–107, http://www.stat.berkeley.edu/users/statcur/Preprints/ ComputingCurric3.pdf.
Rajaraman, A. and Ullman, J. D. (2011), Mining of massive datasets, Cambridge University Press, http://www.mmds.org/.
Rojas, F. (2013), “How Twitter can help predict an election,” http://www. washingtonpost.com/opinions/how-twitter-can-predict-an-election/2013/08/11/ 35ef885a-0108-11e3-96a8-d3b921c0924a_story.html.
RStudio and Inc. (2013), shiny: Web Application Framework for R, r package version 0.8.0, http: //CRAN.R-project.org/package=shiny.
Stanton, J. (2012), An Introduction to Data Science, https://ischool.syr.edu/media/ documents/2012/3/DataScienceBook1_1.pdf.
Swires-Hennessy, E. (2014), Presenting Data: How to Communicate Your Message Effectively, Wiley, 1st ed., http://www.wiley.com/WileyCDA/WileyTitle/productCd-1118489594.html.
Tan, P.-N., Steinbach, M., and Kumar, V. (2006), Introduction to Data Mining, Pearson Addison- Wesley, 1st ed., http://www-users.cs.umn.edu/~kumar/dmbook/index.php.
Tufte, E. R. (1983), The Visual Display of Quantitative Information, Graphics Press, 2nd ed. Wickham, H. (2012), “my cynical definition: a data scientist is a statistician who is useful ;),”
https://twitter.com/hadleywickham/status/263750846246969344.
— (2014), “Tidy data,” The Journal of Statistical Software, 59, http://vita.had.co.nz/papers/
tidy-data.html.
Wickham, H. and Francois, R. (2014), dplyr: a grammar of data manipulation, R package version
0.1, http://CRAN.R-project.org/package=dplyr. 13
Wilkinson, L., Wills, D., Rope, D., Norton, A., and Dubbs, R. (2006), The grammar of graphics, Springer.
Yau, N. (2011), Visualize this: the Flowing Data guide to design, visualization, and statistics, Wiley Publishing.
— (2013), Data points: visualization that means something, John Wiley & Sons.
Zhu, Y., Hernandez, L. M., Mueller, P., Dong, Y., and Forman, M. R. (2013), “Data Acquisition and Preprocessing in Studies on Humans: What is Not Taught in Statistics Classes?” The American Statistician, 67, 235–241, http://dx.doi.org/10.1080/00031305.2013.842498.
36.3
Bernau, C., Riester, M., Boulesteix, A.-L., Parmigiani, G., Huttenhower, C., Waldron, L., and Trippa, L. (2014), “Cross-Study Validation for the Assessment of Prediction Algorithms,” Bioinformatics, 30, i105–i112. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.4
Breiman, L. (2001), “Statistical Modeling: the Two Cultures,” Statistical Science, 16, 199–231. [Crossref], [Web of Science ®], [Google Scholar]
36.5
Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., and Munafò, M. R. (2013), “Power Failure: Why Small Sample Size Undermines the Reliability of Neuroscience,” Nature Reviews Neuroscience, 14, 365–376. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.6
Carp, J. (2012), “The Secret Lives of Experiments: Methods Reporting in the fMRI Literature,” Neuroimage, 63, 289–300. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.7
Chambers, J. M. (1993), “Greater or Lesser Statistics: A Choice for Future Research,” Statistics and Computing, 3, 182–184. [Crossref], [Web of Science ®], [Google Scholar]
36.8
Chavalarias, D., Wallach, J., Li, A., and Ioannidis, J. A. (2016), “Evolution of Reporting p Values in the Biomedical Literature, 1990–2015,” Journal of the American Medical Association, 315, 1141–1148. [Crossref], [Web of Science ®], [Google Scholar]
36.9
Cleveland, W. S. (1985), The Elements of Graphing Data, Monterey, CA: Wadsworth Advanced Books and Software. [Google Scholar]
36.10
Cleveland, W. S. (1993), Visualizing Data, Summit, NJ: Hobart Press. [Google Scholar]
36.11
Cleveland, W. S.(2001), “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics,” International Statistical Review, 69, 21–26. [Crossref], [Web of Science ®], [Google Scholar]
36.12
Coale, A. J., and Stephan, F. F. (1962), “The Case of the Indians and the Teen-Age Widows,” Journal of the American Statistical Association, 57, 338–347. [Taylor & Francis Online], [Web of Science ®], [Google Scholar]
36.13
Collins, F., and Tabak, L. A. (2014), “Policy: NIH Plans to Enhance Reproducibility,” Nature, 505, 612–613. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.14
Cook, D., and Swayne, D. F. (2007), Interactive and Dynamic Graphics for Data Analysis: With R and GGobi, New York: Springer Science & Business Media. [Crossref], [Google Scholar]
36.15
Dettling, M. (2004), “BagBoosting for Tumor Classification with Gene Expression Data,” Bioinformatics, 20, 3583–3593. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.16
Donoho, D., and Jin, J. (2008), “Higher Criticism Thresholding: Optimal Feature Selection When Useful Features Are Rare and Weak,” Proceedings of the National Academy of Sciences, 105, 14790–14795. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
36.17
Donoho, D. L., Maleki, A., Rahman, I. U., Shahram, M., and Stodden, V. (2009), “Reproducible Research in Computational Harmonic Analysis,” Computing in Science and Engineering, 11, 8–18. [Crossref], [Web of Science ®], [Google Scholar]
36.18
Fisher, R. A. (1936), “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, 7, 179–188. [Crossref], [Google Scholar]
36.19
Freire, J., Bonnet, P., and Shasha, D. (2012), “Computational Reproducibility: State-of-the-Art, Challenges, and Database Research Opportunities,” in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD ’12, ACM, pp. 593–596. [Crossref], [Google Scholar]
36.20
Gavish, M. (2012), “Three Dream Applications of Verifiable Computational Results,” Computing in Science & Engineering, 14, 26–31. [Crossref], [Web of Science ®], [Google Scholar]
Gavish, M., and Donoho, D. (2011), “A Universal Identifier for Computational Results,” Procedia Computer Science, 4, 637–647. [Crossref], [Google Scholar]
Hand, D. J. (2006), “Classifier Technology and the Illusion of Progress,” Statistical Science, 21, 1–14. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Harris, H., Murphy, S., and Vaisman, M. (2013), Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work, Sebastopol, CA: O’Reilly Media, Inc. [Google Scholar]
Heroux, M. A. (2015), “Editorial: ACM TOMS Replicated Computational Results Initiative,” ACM Transactions on Mathematical Software, 41, 13:1–13. [Crossref], [Web of Science ®], [Google Scholar]
Horton, N. J., Baumer, B. S., and Wickham, H. (2015), “Taking a Chance in the Classroom: Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics,” CHANCE, 28, 40–50. [Taylor & Francis Online], [Google Scholar]
Hotelling, H. (1940), “The Teaching of Statistics,” The Annals of Mathematical Statistics, 11, 457–470. [Crossref], [Google Scholar]
Ioannidis, J. P. A. (2005), “Contradicted and Initially Stronger Effects in Highly Cited Clinical Research,” Journal of the American Medical Association, 294, 218–228. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
——— (2007), “Non-Replication and Inconsistency in the Genome-Wide Association Setting,” Human Heredity, 64, 203–213. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
——— (2008), “Why Most Discovered True Associations are Inflated,” Epidemiology, 19, 640–648. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Iverson, K. E. (1991), “A Personal View of APL,” IBM Systems Journal, 30, 582–593. [Crossref], [Google Scholar]
Jager, L. R., and Leek, J. T. (2014), “An Estimate of the Science-Wise False Discovery Rate and Application to The Top Medical Literature,” Biostatistics, 15, 1–12. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Liberman, M. (2010), “Fred Jelinek,” Computational Linguistics, 36, 595–599. [Crossref], [Web of Science ®], [Google Scholar]
Madigan, D., Stang, P. E., Berlin, J. A., Schuemie, M., Overhage, J. M., Suchard, M. A., Dumouchel, B., Hartzema, A. G., and Ryan, P. B. (2014), “A Systematic Statistical Approach to Evaluating Evidence From Observational Studies,” Annual Review of Statistics and Its Application, 1, 11–39. [Crossref], [Web of Science ®], [Google Scholar]
Marchi, M., and Albert, J. (2013), Analyzing Baseball Data with R, Boca Raton, FL: CRC Press. [Google Scholar]
McNutt, M. (2014), “Reproducibility,” Science, 343, 229. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Mosteller, F., and Tukey, J. W. (1968), “Data Analysis, Including Statistics,” in Handbook of Social Psychology (Vol. 2), eds. G. Lindzey, and E. Aronson, Reading, MA: Addison-Wesley, pp. 80–203. [Google Scholar]
Open Science Collaboration et al. (2015), “Estimating the Reproducibility of Psychological Science,” Science, 349, aac4716. [Crossref], [Web of Science ®], [Google Scholar]
Pan, Z., Trikalinos, T. A., Kavvoura, F. K., Lau, J., and Ioannidis, J. P. A. (2005), “Local Literature Bias in Genetic Epidemiology: An Empirical Evaluation of the Chinese Literature,” PLoS Medicine, 2, 1309. [Crossref], [Web of Science ®], [Google Scholar]
Peng, R. D. (2009), “Reproducible Research and Biostatistics,” Biostatistics, 10, 405–408. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Prinz, F., Schlange, T., and Asadullah, K. (2011), “Believe It or Not: How Much Can We Rely on Published Data on Potential Drug Targets?” Nature Reviews Drug Discovery, 10, 712–712. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Ryan, P. B., Madigan, D., Stang, P. E., Overhage, J. M., Racoosin, J. A., and Hartzema, A. G. (2012), “Empirical Assessment of Methods for Risk Identification in Healthcare Data: Results From the Experiments of the Observational Medical Outcomes Partnership,” Statistics in Medicine, 31, 4401–4415. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Stodden, V. (2012), “Reproducible Research: Tools and Strategies for Scientific Computing,” Computing in Science and Engineering, 14, 11–12. [Crossref], [Web of Science ®], [Google Scholar]
Stodden, V., Guo, P., and Ma, Z. (2013), “Toward Reproducible Computational Research: An Empirical Analysis of Data and Code Policy Adoption by Journals,” PLoS ONE, 8, e67111. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Stodden, V., Leisch, F., and Peng, R. D., editors. (2014), Implementing Reproducible Research, Boca Raton, FL: Chapman & Hall/CRC. [Crossref], [Google Scholar]
Stodden, V., and Miguez, S. (2014), “Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research,” Journal of Open Research Software, 1, e21. [Crossref], [Google Scholar]
Sullivan, P. F. (2007), “Spurious Genetic Associations,” Biological Psychiatry, 61, 1121–1126. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
Tango, T. M., Lichtman, M. G., and Dolphin, A. E. (2007), The Book: Playing the Percentages in Baseball, Lincoln, NE: Potomac Books, Inc. [Google Scholar]
Tukey, J. W. (1962), “The Future of Data Analysis,” The Annals of Mathematical Statistics, 33, 1–67. [Crossref], [Google Scholar]
——— (1977), Exploratory Data Analysis, Reading, MA: Addison-Wesley. [Google Scholar]
——— (1994), The Collected Works of John W. Tukey: Multiple Comparisons (Vol. 1), eds. H. I. Braun, Pacific Grove, CA: Wadsworth & Brooks/Cole. [Google Scholar]
Wandell, B. A., Rokem, A., Perry, L. M., Schaefer, G., and Dougherty, R. F. (2015), “Quantitative Biology – Quantitative Methods,” Bibliographic Code: 2015arXiv150206900W. [Google Scholar]
Wickham, H. (2007), “Reshaping Data With the Reshape Package,” Journal of Statistical Software, 21, 1–20. [Crossref], [Web of Science ®], [Google Scholar]
——— (2011), “ggplot2,” Wiley Interdisciplinary Reviews: Computational Statistics, 3, 180–185. [Crossref], [Google Scholar]
——— (2011), “The Split-Apply-Combine Strategy for Data Analysis,” Journal of Statistical Software, 40, 1–29. [Web of Science ®], [Google Scholar]
——— (2014), “Tidy Data,” Journal of Statistical Software, 59, 1–23. [PubMed], [Web of Science ®], [Google Scholar]
Wilkinson, L. (2006), The Grammar of Graphics, New York: Springer Science & Business Media. [Google Scholar]
Zhao, S. D., Parmigiani, G., Huttenhower, C., and Waldron, L. (2014), “Más-o-Menos: A Simple Sign Averaging Method for Discrimination in Genomic Data Analysis,” Bioinformatics, 30, 3062–3069. [Crossref], [PubMed], [Web of Science ®], [Google Scholar]
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参考資料(References)
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