IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v72y2010i4p405-416.html
   My bibliography  Save this article

Discovering the false discovery rate

Author

Listed:
  • Yoav Benjamini

Abstract

Summary. I describe the background for the paper ‘Controlling the false discovery rate: a new and powerful approach to multiple comparisons’ by Benjamini and Hochberg that was published in the Journal of the Royal Statistical Society, Series B, in 1995. I review the progress since made on the false discovery rate, as well as the major conceptual developments that followed.

Suggested Citation

  • Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
  • Handle: RePEc:bla:jorssb:v:72:y:2010:i:4:p:405-416
    DOI: 10.1111/j.1467-9868.2010.00746.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9868.2010.00746.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1467-9868.2010.00746.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Biometrika Trust, vol. 93(3), pages 491-507, September.
    2. Benjamini, Yoav & Heller, Ruth, 2007. "False Discovery Rates for Spatial Signals," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1272-1281, December.
    3. 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.
    4. Christopher R. Genovese & Kathryn Roeder & Larry Wasserman, 2006. "False discovery control with p-value weighting," Biometrika, Biometrika Trust, vol. 93(3), pages 509-524, September.
    5. Yoav Benjamini & Daniel Yekutieli, 2005. "False Discovery Rate-Adjusted Multiple Confidence Intervals for Selected Parameters," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 71-81, March.
    6. Abramovich, Felix & Benjamini, Yoav, 1996. "Adaptive thresholding of wavelet coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 351-361, August.
    7. Yoav Benjamini & Yosef Hochberg, 2000. "On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics," Journal of Educational and Behavioral Statistics, , vol. 25(1), pages 60-83, March.
    8. Perone Pacifico, M. & Genovese, C. & Verdinelli, I. & Wasserman, L., 2007. "Scan clustering: A false discovery approach," Journal of Multivariate Analysis, Elsevier, vol. 98(7), pages 1441-1469, August.
    9. Nicolai Meinshausen, 2008. "Hierarchical testing of variable importance," Biometrika, Biometrika Trust, vol. 95(2), pages 265-278.
    10. Christopher Genovese & Larry Wasserman, 2002. "Operating characteristics and extensions of the false discovery rate procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 499-517, August.
    11. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    12. Yekutieli, Daniel, 2008. "Hierarchical False Discovery RateControlling Methodology," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 309-316, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Challet, Damien & Bongiorno, Christian & Pelletier, Guillaume, 2021. "Financial factors selection with knockoffs: Fund replication, explanatory and prediction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    2. Djalel-Eddine Meskaldji & Dimitri Van De Ville & Jean-Philippe Thiran & Stephan Morgenthaler, 2020. "A comprehensive error rate for multiple testing," Statistical Papers, Springer, vol. 61(5), pages 1859-1874, October.
    3. Ruitong Li & Olaf Klingbeil & Davide Monducci & Michael J. Young & Diego J. Rodriguez & Zaid Bayyat & Joshua M. Dempster & Devishi Kesar & Xiaoping Yang & Mahdi Zamanighomi & Christopher R. Vakoc & Ta, 2022. "Comparative optimization of combinatorial CRISPR screens," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    4. Chang Yu & Daniel Zelterman, 2020. "Distributions associated with simultaneous multiple hypothesis testing," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-17, December.
    5. George Grekousis & Stelios Gialis, 2019. "More Flexible Yet Less Developed? Spatio-Temporal Analysis of Labor Flexibilization and Gross Domestic Product in Crisis-Hit European Union Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(2), pages 505-524, June.
    6. Nicholas Tibor Longford, 2016. "Decision Theory Applied to Selecting the Winners, Ranking, and Classification," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 420-442, August.
    7. Michelle Acampora & Francesco Capozza & Vahid Moghani, 2022. "Mental Health Literacy, Beliefs and Demand for Mental Health Support among University Students," Tinbergen Institute Discussion Papers 22-079/I, Tinbergen Institute.
    8. Pedro Galeano & Daniel Peña, 2019. "Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 289-329, June.
    9. Chen, Yanhua & Li, Youwei & Pantelous, Athanasios A. & Stanley, H. Eugene, 2022. "Short-run disequilibrium adjustment and long-run equilibrium in the international stock markets: A network-based approach," International Review of Financial Analysis, Elsevier, vol. 79(C).
    10. Schofield, Gillian & Biggart, Laura & Ward, Emma & Larsson, Birgit, 2015. "Looked after children and offending: An exploration of risk, resilience and the role of social cognition," Children and Youth Services Review, Elsevier, vol. 51(C), pages 125-133.
    11. George Grekousis, 2018. "Further Widening or Bridging the Gap? A Cross-Regional Study of Unemployment across the EU Amid Economic Crisis," Sustainability, MDPI, vol. 10(6), pages 1-18, May.
    12. Andrew Y. Chen, 2021. "The Limits of p‐Hacking: Some Thought Experiments," Journal of Finance, American Finance Association, vol. 76(5), pages 2447-2480, October.
    13. Andrew Y. Chen, 2022. "Do t-Statistic Hurdles Need to be Raised?," Papers 2204.10275, arXiv.org, revised Apr 2024.
    14. Marta Cousido‐Rocha & Jacobo de Uña‐Álvarez & Sebastian Döhler, 2022. "Multiple comparison procedures for discrete uniform and homogeneous tests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(1), pages 219-243, January.
    15. Francesca Greselin & Salvatore Ingrassia & Antonio Punzo, 2011. "Assessing the pattern of covariance matrices via an augmentation multiple testing procedure," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(2), pages 141-170, June.
    16. Qingying Zong & Jonathan R. Bradley, 2023. "Criterion constrained Bayesian hierarchical models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 294-320, March.
    17. Georgios Sermpinis & Arman Hassanniakalager & Charalampos Stasinakis & Ioannis Psaradellis, 2018. "Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices," Papers 1811.06766, arXiv.org, revised Jun 2019.
    18. Stefania Capecchi & Maria Iannario & Rosaria Simone, 2018. "Well-Being and Relational Goods: A Model-Based Approach to Detect Significant Relationships," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 729-750, January.

    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. Qingyun Cai & Hock Peng Chan, 2017. "A Double Application of the Benjamini-Hochberg Procedure for Testing Batched Hypotheses," Methodology and Computing in Applied Probability, Springer, vol. 19(2), pages 429-443, June.
    2. T. Tony Cai & Wenguang Sun, 2017. "Optimal screening and discovery of sparse signals with applications to multistage high throughput studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(1), pages 197-223, January.
    3. Cai, Qingyun, 2018. "A scoring criterion for rejection of clustered p-values," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 180-189.
    4. Haibing Zhao & Wing Kam Fung, 2018. "Controlling mixed directional false discovery rate in multidimensional decisions with applications to microarray studies," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(2), pages 316-337, June.
    5. Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
    6. Guo Wenge & Peddada Shyamal, 2008. "Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-21, March.
    7. Zhao, Haibing & Fung, Wing Kam, 2016. "A powerful FDR control procedure for multiple hypotheses," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 60-70.
    8. Habiger, Joshua D. & Peña, Edsel A., 2014. "Compound p-value statistics for multiple testing procedures," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 153-166.
    9. 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.
    10. Guillermo Durand & Gilles Blanchard & Pierre Neuvial & Etienne Roquain, 2020. "Post hoc false positive control for structured hypotheses," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(4), pages 1114-1148, December.
    11. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(3), pages 417-442, November.
    12. 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 1-56, January.
    13. Andrew Y. Chen & Tom Zimmermann, 2022. "Publication Bias in Asset Pricing Research," Papers 2209.13623, arXiv.org, revised Sep 2023.
    14. Yu Xiaoqing & Sun Shuying, 2016. "Comparing five statistical methods of differential methylation identification using bisulfite sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(2), pages 173-191, April.
    15. Christina C. Bartenschlager & Jens O. Brunner, 2019. "Reaching for the stars: attention to multiple testing problems and method recommendations using simulation for business research," Journal of Business Economics, Springer, vol. 89(4), pages 447-479, June.
    16. Zehetmayer Sonja & Graf Alexandra C. & Posch Martin, 2015. "Sample size reassessment for a two-stage design controlling the false discovery rate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(5), pages 429-442, November.
    17. Nik Tuzov & Frederi Viens, 2011. "Mutual fund performance: false discoveries, bias, and power," Annals of Finance, Springer, vol. 7(2), pages 137-169, May.
    18. Debashis Ghosh & Wei Chen & Trivellore Raghuanthan, 2004. "The false discovery rate: a variable selection perspective," The University of Michigan Department of Biostatistics Working Paper Series 1040, Berkeley Electronic Press.
    19. Habiger, Joshua D. & Adekpedjou, Akim, 2014. "Optimal rejection curves for exact false discovery rate control," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 21-28.
    20. Guo, Wenge & Bhaskara Rao, M., 2008. "On optimality of the Benjamini-Hochberg procedure for the false discovery rate," Statistics & Probability Letters, Elsevier, vol. 78(14), pages 2024-2030, October.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssb:v:72:y:2010:i:4:p:405-416. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.