IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i17p2154-d628663.html
   My bibliography  Save this article

On the Convergence of the Benjamini–Hochberg Procedure

Author

Listed:
  • Dean Palejev

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
    Big Data for Smart Society (GATE) Institute, Sofia University “St. Kliment Ohridski”, 1113 Sofia, Bulgaria)

  • Mladen Savov

    (Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
    Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, 1164 Sofia, Bulgaria)

Abstract

The Benjamini–Hochberg procedure is one of the most used scientific methods up to date. It is widely used in the field of genetics and other areas where the problem of multiple comparison arises frequently. In this paper we show that under fairly general assumptions for the distribution of the test statistic under the alternative hypothesis, when increasing the number of tests, the power of the Benjamini–Hochberg procedure has an exponential type of asymptotic convergence to a previously shown limit of the power. We give a theoretical lower bound for the probability that for a fixed number of tests the power is within a given interval around its limit together with a software routine that calculates these values. This result is important when planning costly experiments and estimating the achieved power after performing them.

Suggested Citation

  • Dean Palejev & Mladen Savov, 2021. "On the Convergence of the Benjamini–Hochberg Procedure," Mathematics, MDPI, vol. 9(17), pages 1-19, September.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2154-:d:628663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/17/2154/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/17/2154/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Richard Van Noorden & Brendan Maher & Regina Nuzzo, 2014. "The top 100 papers," Nature, Nature, vol. 514(7524), pages 550-553, October.
    2. Anastasia Chalkidou & Michael J O’Doherty & Paul K Marsden, 2015. "False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
    3. Ferreira José António & Zwinderman Aeilko H, 2006. "Approximate Power and Sample Size Calculations with the Benjamini-Hochberg Method," The International Journal of Biostatistics, De Gruyter, vol. 2(1), pages 1-38, September.
    4. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    5. Alessio Farcomeni, 2007. "Some Results on the Control of the False Discovery Rate under Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 275-297, June.
    6. Gontscharuk, Veronika & Finner, Helmut, 2013. "Asymptotic FDR control under weak dependence: A counterexample," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1888-1893.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Xiongzhi, 2020. "A strong law of large numbers for simultaneously testing parameters of Lancaster bivariate distributions," Statistics & Probability Letters, Elsevier, vol. 167(C).
    2. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    3. Ferreira José A. & Berkhof Johannes & Souverein Olga & Zwinderman Koos, 2009. "A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-58, January.
    4. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    5. Gontscharuk, Veronika & Finner, Helmut, 2013. "Asymptotic FDR control under weak dependence: A counterexample," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1888-1893.
    6. T. Tony Cai & Weidong Liu, 2016. "Large-Scale Multiple Testing of Correlations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 229-240, March.
    7. Lucy Semerjian & Kunle Okaiyeto & Mike O. Ojemaye & Temitope Cyrus Ekundayo & Aboi Igwaran & Anthony I. Okoh, 2021. "Global Systematic Mapping of Road Dust Research from 1906 to 2020: Research Gaps and Future Direction," Sustainability, MDPI, vol. 13(20), pages 1-21, October.
    8. Tsukasa Kato, 2021. "Measurement Invariance in the Center for Epidemiologic Studies-Depression (CES-D) Scale among English-Speaking Whites and Asians," IJERPH, MDPI, vol. 18(10), pages 1-10, May.
    9. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    10. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    11. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    12. Hanck, Christoph, 2011. "Now, whose schools are really better (or weaker) than Germany's? A multiple testing approach," Economic Modelling, Elsevier, vol. 28(4), pages 1739-1746, July.
    13. Ghosh Debashis, 2012. "Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-21, July.
    14. Song Li & Mervyn J. Silvapulle & Param Silvapulle & Xibin Zhang, 2015. "Bayesian Approaches to Nonparametric Estimation of Densities on the Unit Interval," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 394-412, March.
    15. Moon, H.R. & Perron, B., 2012. "Beyond panel unit root tests: Using multiple testing to determine the nonstationarity properties of individual series in a panel," Journal of Econometrics, Elsevier, vol. 169(1), pages 29-33.
    16. Sermpinis, Georgios & Hassanniakalager, Arman & Stasinakis, Charalampos & Psaradellis, Ioannis, 2021. "Technical analysis profitability and Persistence: A discrete false discovery approach on MSCI indices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    17. Alessio Farcomeni & Antonio Punzo, 2020. "Robust model-based clustering with mild and gross outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 989-1007, December.
    18. Chunming Zhang, 2014. "Assessing mean and median filters in multiple testing for large-scale imaging data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(1), pages 51-71, March.
    19. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    20. Kang, Moonsu & Chun, Heuiju, 2011. "A generalized false discovery rate in microarray studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 731-737, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2154-:d:628663. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.