A Not-so-radical Rejoinder: Habituate Systems Thinking and Data (Science) Confession for Quality Enhancement✩
Pub. online: 6 January 2023 Type: Statistical Methodology Open Access
✩ Main article: https://doi.org/10.51387/22-NEJSDS6.
7 September 2022
7 September 2022
6 January 2023
6 January 2023
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
Boyd, R. J., Powney, G. D. and Pescott, O. L. We need to talk about nonprobability samples (2022). arXiv preprint. arXiv:2210.07298.
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.
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.
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.
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.
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.
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.
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.
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.
Leonelli, S. Data Science in Times of Pan(dem)ic. Harvard Data Science Review 3(1) (2021). https://hdsr.mitpress.mit.edu/pub/fi1rol2i.
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.
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.
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.
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.
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.
Meng, X.-L. Data science: An artificial ecosystem. Harvard Data Science Review 1(1), 7 (2019). https://doi.org/10.1162/99608f92.ba20f892.
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.
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.
Msaouel, P. The big data paradox in clinical practice. Cancer Investigation 40(7) 567–576 (2022). https://doi.org/10.1080/07357907.2022.2084621.
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.
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.
Su, W. A truthful owner-assisted scoring mechanism (2022). arXiv preprint arXiv:2206.08149.