The New England Journal of Statistics in Data Science logo


  • Help
Login Register

  1. Home
  2. To appear
  3. A Not-so-radical Rejoinder: Habituate Sy ...

The New England Journal of Statistics in Data Science

Submit your article Information Become a Peer-reviewer
  • Article info
  • Full article
  • More
    Article info Full article

A Not-so-radical Rejoinder: Habituate Systems Thinking and Data (Science) Confession for Quality Enhancement✩
Xiao-Li Meng  

Authors

 
Placeholder
https://doi.org/10.51387/22-NEJSDS6REJ
Pub. online: 6 January 2023      Type: Statistical Methodology      Open accessOpen Access

✩ Main article: https://doi.org/10.51387/22-NEJSDS6.

Accepted
7 September 2022
Published
6 January 2023

References

[1] 
Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C. et al.Redefine statistical significance. Nature Human Behaviour 2. 6–10 (2018).
[2] 
Blocker, A. W. and Meng, X.-L. The potential and perils of preprocessing: Building new foundations. Bernoulli 19(4) 1176–1211 (2013). https://doi.org/10.3150/13-BEJSP16. MR3102548
[3] 
Boyd, R. J., Powney, G. D. and Pescott, O. L. We need to talk about nonprobability samples (2022). arXiv preprint. arXiv:2210.07298.
[4] 
Bradley, Valerie C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X.-L. and Flaxman, S. Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature 600(7890) 695–700 (2021).
[5] 
Conklin, J. Dialogue mapping: Building shared understanding of wicked problems. John Wiley & Sons, Inc., (2005).
[6] 
Cook-Deegan, R. M. The gene wars: Science, politics, and the human genome. WW Norton & Company, (1994).
[7] 
Craiu, R. V., Gong, R. and Meng, X.-L. Six statistical senses. Annual Review of Statistics and Its Application. in press. https://doi.org/10.1146/annurevstatistics-040220-015348.
[8] 
Dahleh, M. A. Data Science in Domains: Interaction of Physical, Social, and Institutional Systems. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/nv9xq9yu.
[9] 
Dwork, C. Differential privacy: A survey of results. In International Conference on Theory and Applications of Models of Computation 1–19. Springer, (2008). https://doi.org/10.1007/978-3-540-79228-4_1. MR2472670.
[10] 
Dwork, C. and Smith, A. Differential privacy for statistics: What we know and what we want to learn. Journal of Privacy and Confidentiality 1(2) 135–154 (2009). https://doi.org/10.1007/978-3-540-79228-4_1. MR2472670.
[11] 
Gong, R., Groshen, E. L. and Vadhan, S. Harnessing the Known Unknowns: Differential Privacy and the 2020 Census. Harvard Data Science Review, (Special Issue 2), (2022). https://hdsr.mitpress.mit.edu/pub/fgyf5cne.
[12] 
Haas, L., Hero, A. and Lue, R. A. Highlights of the National Academies Report on “Undergraduate Data Science: Opportunities and Options? Harvard Data Science Review 1(1) (2019). https://doi.org/10.1162/99608f92.38f16b68.
[13] 
Junk, T. R. and Lyons, L. Reproducibility and replication of experimental particle physics results. Harvard Data Science Review 2(4) (2020). https://doi.org/10.1162/99608f92.250f995b.
[14] 
Kifer, D. and Machanavajjhala, A. No free lunch in data privacy. In Proceedings of the 2011 International Conference on Management of Data – SIGMOD ’11, Athens, Greece 193–204 (2011). ACM Press. https://doi.org/10.1145/1989323.1989345.
[15] 
Larson, E. J. The Myth of Artificial Intelligence. Harvard University Press, (2021).
[16] 
Leonelli, S. Data Science in Times of Pan(dem)ic. Harvard Data Science Review 3(1) (2021). https://hdsr.mitpress.mit.edu/pub/fi1rol2i.
[17] 
Li, X. and Meng, X.-L. A multi-resolution theory for approximating infinite-p-zero-n: Transitional inference, individualized predictions, and a world without bias-variance tradeoff. Journal of the American Statistical Association 116(533) 353–367 (2021). https://doi.org/10.1080/01621459.2020.1844210. MR4227699.
[18] 
Lindsey, J. K. Some statistical heresies (with discussion). Journal of the Royal Statistical Society: Series D (The Statistician) 48(1) 1–40 (1999).
[19] 
Meng, X.-L. Multiple-imputation inferences with uncongenial sources of input (with discussion). Statistical Science 9(4) 538–558 (1994). http://www.jstor.org/stable/10.2307/2246252.
[20] 
Meng, X.-L. Double effort, not double blind! Technical Report 382, Department of Statistics, University of Chicago (1994).
[21] 
Meng, X.-L. Desired and feared — what do we do now and over the next 50 years? The American Statistician 63(3) 202–210 (2009). https://doi.org/10.1198/tast.2009.09045. MR2750343.
[22] 
Meng, X.-L. A trio of inference problems that could win you a Nobel prize in statistics (if you help fund it). In Past, Present, and Future of Statistical Science (Eds: Lin et. al.) CRC Press, (2014). MR2049935.
[23] 
Meng, X.-L. Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. Annals of Applied Statistics 12(2) 685–726 (2018). https://doi.org/10.1214/18-AOAS1161SF. MR3834282.
[24] 
Meng, X.-L. Data science: An artificial ecosystem. Harvard Data Science Review 1(1), 7 (2019). https://doi.org/10.1162/99608f92.ba20f892.
[25] 
Meng, X.-L. Building Data Science Infrastructures and Infrastructural Data Science. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/kdqoo5ax.
[26] 
Meng, X.-L. Enhancing (publications on) data quality: Deeper data minding and fuller data confession. Journal of the Royal Statistical Society: Series A (Statistics in Society) 184(4) 1161–1175 (2021). https://doi.org/10.1111/rssa.12762.
[27] 
Msaouel, P. The big data paradox in clinical practice. Cancer Investigation 40(7) 567–576 (2022). https://doi.org/10.1080/07357907.2022.2084621.
[28] 
Qin, S. J. Data Science Education With Domain Knowledge and System Principles. Harvard Data Science Review 3(2) (2021). https://hdsr.mitpress.mit.edu/pub/8si074w9.
[29] 
Rubin, D. B. Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics 12 1151–1172 (1984). https://doi.org/10.1214/aos/1176346785. MR0760681.
[30] 
Stein, M. and Meng, X.-L. Report on 1995 IMS-ASA invited panel on Speeding the referee process. IMS Bulletin 24. 607–608 (1995).
[31] 
Su, W. You are the best reviewer of your own papers: An owner-assisted scoring mechanism. Advances in Neural Information Processing Systems 34. 27929–27939 (2021).
[32] 
Su, W. A truthful owner-assisted scoring mechanism (2022). arXiv preprint arXiv:2206.08149.

Full article PDF XML
Full article PDF XML

Copyright
© 2023 New England Statistical Society
by logo by logo
Open access article under the CC BY license.

Metrics (since February 2017)
124

Article info
views

35

Full article
views

49

PDF
downloads

16

XML
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

The New England Journal of Statistics in Data Science

  • ISSN: 2693-7166
  • Copyright © 2021 New England Statistical Society

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer
Powered by PubliMill  •  Privacy policy