1,034
Views
4
CrossRef citations to date
0
Altmetric
Theory and Methods

Structure–Adaptive Sequential Testing for Online False Discovery Rate Control

, &
Pages 732-745 | Received 25 Feb 2020, Accepted 09 Jul 2021, Published online: 17 Nov 2021

References

  • Aharoni, E., Neuvirth, H., and Rosset, S. (2010), “The Quality Preserving Database: A Computational Framework for Encouraging Collaboration, Enhancing Power and Controlling False Discovery,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8, 1431–1437. DOI: 10.1109/TCBB.2010.105.
  • Aharoni, E., and Rosset, S. (2014), “Generalized α-Investing: Definitions, Optimality Results and Application to Public Databases,” Journal of the Royal Statistical Society, Series B, 76, 771–794. DOI: 10.1111/rssb.12048.
  • Ahmad, S., Lavin, A., Purdy, S., and Agha, Z. (2017), “Unsupervised Real-Time Anomaly Detection for Streaming Data,” Neurocomputing, 262, 134–147. DOI: 10.1016/j.neucom.2017.04.070.
  • Basu, P., Cai, T. T., Das, K., and Sun, W. (2018), “Weighted False Discovery Rate Control in Large-Scale Multiple Testing,” Journal of the American Statistical Association, 113, 1172–1183. DOI: 10.1080/01621459.2017.1336443.
  • Benjamini, Y., and Hochberg, Y. (1995), “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” Journal of Royal Statistical Society, Series B, 57, 289–300. DOI: 10.1111/j.2517-6161.1995.tb02031.x.
  • Cai, T. T., and Sun, W. (2009), “Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks,” Journal of American Statistical Association, 104, 1467–1481. DOI: 10.1198/jasa.2009.tm08415.
  • Cai, T. T., Sun, W., and Wang, W. (2019), “CARS: Covariate Assisted Ranking and Screening for Large-Scale Two-Sample Inference (with discussion),” Journal of Royal Statistical Society, Series B, 81, 187–234. DOI: 10.1111/rssb.12304.
  • Cai, T. T., Sun, W., and Xia, Y. (2021), “Laws: A Locally Adaptive Weighting and Screening Approach to Spatial Multiple Testing,” Journal of the American Statistical Association, to appear, DOI: 10.1080/01621459.2020.1859379.
  • Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I. (1990), “Stl: A Seasonal-Trend Decomposition,” Journal of Official Statistics, 6, 3–73.
  • Efron, B. (2004), “Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis,” Journal of the American Statistical Association, 99, 96–104. DOI: 10.1198/016214504000000089.
  • Foster, D. P., and Stine, R. A. (2008), “α -Investing: A Procedure for Sequential Control of Expected False Discoveries,” Journal of the Royal Statistical Society, Series B, 70, 429–444.
  • Genovese, C. R., Roeder, K., and Wasserman, L. (2006), “False Discovery Control With p-Value Weighting,” Biometrika, 93, 509–524. DOI: 10.1093/biomet/93.3.509.
  • Holm, S. (1979), “A Simple Sequentially Rejective Multiple Test Procedure,” Scandinavian Journal of Statistics, 6, 65–70.
  • Hu, J. X., Zhao, H., and Zhou, H. H. (2010), “False Discovery Rate Control With Groups,” Journal of the American Statistical Association, 105, 1215–1227. DOI: 10.1198/jasa.2010.tm09329.
  • Javanmard, A., and Montanari, A. (2018), “Online Rules for Control of False Discovery Rate and False Discovery Exceedance,” The Annals of Statistics, 46, 526–554. DOI: 10.1214/17-AOS1559.
  • Jin, J., and Cai, T. T. (2007), “Estimating the Null and the Proportional of Nonnull Effects in Large-Scale Multiple Comparisons,” Journal of the American Statistical Association, 102, 495–506. DOI: 10.1198/016214507000000167.
  • Karp, N. A., Mason, J., Beaudet, A. L., Benjamini, Y., Bower, L., Braun, R. E., Brown, S. D., Chesler, E. J., Dickinson, M. E., Flenniken, A. M., Fuchs, H., Angelis, M. H., Gao, X., Guo, S., Greenaway, S., Heller, R., Herault, Y., Justice, M. J., Kurbatova, N., Lelliott, C. J., Lloyd, K. C. K., Mallon, A. M., Mank, J. E., Masuya, H., McKerlie, C., Meehan, T. F., Mott, R. F., Murray, S. A., Parkinson, H., Ramirez-Solis, R., Santos, L., Seavitt, J. R., Smedley, D., Sorg, T., Speak, A. O., Steel, K. P., Svenson, K. L., International Mouse Phenotyping Consortium, Wakana, S., West, D., Wells, S., Westerberg, H., Yaacoby, S., and White, J. K. (2017), “Prevalence of Sexual Dimorphism in Mammalian Phenotypic Traits,” Nature Communications, 8, 15475. DOI: 10.1038/ncomms15475.
  • Lei, L., and Fithian, W. (2018), “Adapt: An Interactive Procedure for Multiple Testing With Side Information,” Journal of the Royal Statistical Society, Series B, 80, 649–679. DOI: 10.1111/rssb.12274.
  • Li, A., and Barber, R. F. (2019), “Multiple Testing With the Structure-Adaptive Benjamini–Hochberg Algorithm,” Journal of the Royal Statistical Society, Series B, 81, 45–74. DOI: 10.1111/rssb.12298.
  • Lynch, G., Guo, W., Sarkar, S. K., and Finner, H. (2017), “The Control of the False Discovery Rate in Fixed Sequence Multiple Testing,” Electronic Journal of Statistics, 11, 4649–4673. DOI: 10.1214/17-EJS1359.
  • Ramdas, A., Yang, F., Wainwright, M. J., and Jordan, M. I. (2017), “Online Control of the False Discovery Rate With Decaying Memory,” in Advances in Neural Information Processing Systems, eds. I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, La Jolla, CA: Neural Information Processing Systems Conference, pp. 5650–5659.
  • Ramdas, A., Zrnic, T., Wainwright, M., and Jordan, M. (2018), “Saffron: An Adaptive Algorithm for Online Control of the False Discovery Rate,” in International Conference on Machine Learning, Stockholm, Sweden, pp. 4286–4294.
  • Robertson, D. S., and Wason, J. (2018), “Online Control of the False Discovery Rate in Biomedical Research,” arXiv: 1809.07292.
  • Silverman, B. W. (1986), Density Estimation for Statistics and Data Analysis, Vol. 26. London: CRC Press.
  • Stein, M. L. (2012), Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer.
  • Sun, W., and Cai, T. T. (2007), “Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control,” Journal of American Statistical Association, 102, 901–912. DOI: 10.1198/016214507000000545.
  • Tian, J., and Ramdas, A. (2019), “Addis: An Adaptive Discarding Algorithm for Online FDR Control With Conservative Nulls,” in Advances in Neural Information Processing Systems, eds. H. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. Fox, and R. Garnett, San Diego, CA: Neural Information Processing Systems Conference, pp. 9388– 9396.
  • Wand, M. P., and Jones, M. C. (1994), Kernel Smoothing, Boca Raton, FL: Chapman and Hall/CRC.
  • Xia, Y., Cai, T. T., and Sun, W. (2020), “Gap: A General Framework for Information Pooling in Two-Sample Sparse Inference,” Journal of the American Statistical Association, 115, 1236–1250. DOI: 10.1080/01621459.2019.1611585.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.