IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v102y2015i4p753-766..html
   My bibliography  Save this article

Optimal multiple testing under a Gaussian prior on the effect sizes

Author

Listed:
  • Edgar Dobriban
  • Kristen Fortney
  • Stuart K. Kim
  • Art B. Owen

Abstract

We develop a new method for large-scale frequentist multiple testing with Bayesian prior information. We find optimal $p$-value weights that maximize the average power of the weighted Bonferroni method. Due to the nonconvexity of the optimization problem, previous methods that account for uncertain prior information are suitable for only a small number of tests. For a Gaussian prior on the effect sizes, we give an efficient algorithm that is guaranteed to find the optimal weights nearly exactly. Our method can discover new loci in genome-wide association studies and compares favourably to competitors. An open-source implementation is available.

Suggested Citation

  • Edgar Dobriban & Kristen Fortney & Stuart K. Kim & Art B. Owen, 2015. "Optimal multiple testing under a Gaussian prior on the effect sizes," Biometrika, Biometrika Trust, vol. 102(4), pages 753-766.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:753-766.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asv050
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rubin Daniel & Dudoit Sandrine & van der Laan Mark, 2006. "A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-20, August.
    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. Ruth Heller & Abba Krieger & Saharon Rosset, 2023. "Optimal multiple testing and design in clinical trials," Biometrics, The International Biometric Society, vol. 79(3), pages 1908-1919, September.
    2. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    3. Saharon Rosset & Ruth Heller & Amichai Painsky & Ehud Aharoni, 2022. "Optimal and maximin procedures for multiple testing problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1105-1128, September.

    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. Jelle J Goeman & Aldo Solari, 2024. "On selection and conditioning in multiple testing and selective inference," Biometrika, Biometrika Trust, vol. 111(2), pages 393-416.
    2. Jordà, Òscar & Knüppel, Malte & Marcellino, Massimiliano, 2013. "Empirical simultaneous prediction regions for path-forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 456-468.
    3. Michael A. Langston & Robert S. Levine & Barbara J. Kilbourne & Gary L. Rogers & Anne D. Kershenbaum & Suzanne H. Baktash & Steven S. Coughlin & Arnold M. Saxton & Vincent K. Agboto & Darryl B. Hood &, 2014. "Scalable Combinatorial Tools for Health Disparities Research," IJERPH, MDPI, vol. 11(10), pages 1-25, October.
    4. Miecznikowski, Jeffrey C. & Gold, David & Shepherd, Lori & Liu, Song, 2011. "Deriving and comparing the distribution for the number of false positives in single step methods to control k-FWER," Statistics & Probability Letters, Elsevier, vol. 81(11), pages 1695-1705, November.
    5. Nikolaos Ignatiadis & Wolfgang Huber, 2021. "Covariate powered cross‐weighted multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(4), pages 720-751, September.
    6. DiCiccio, Cyrus J. & DiCiccio, Thomas J. & Romano, Joseph P., 2020. "Exact tests via multiple data splitting," Statistics & Probability Letters, Elsevier, vol. 166(C).
    7. T. Tony Cai & Wenguang Sun & Weinan Wang, 2019. "Covariate‐assisted ranking and screening for large‐scale two‐sample inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 187-234, April.
    8. Simeone Marino & Yi Zhao & Nina Zhou & Yiwang Zhou & Arthur W Toga & Lu Zhao & Yingsi Jian & Yichen Yang & Yehu Chen & Qiucheng Wu & Jessica Wild & Brandon Cummings & Ivo D Dinov, 2020. "Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    9. Kang Guolian & Ye Keying & Liu Nianjun & Allison David B. & Gao Guimin, 2009. "Weighted Multiple Hypothesis Testing Procedures," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-24, April.
    10. Shi, Chengchun & Li, Lexin, 2022. "Testing mediation effects using logic of Boolean matrices," LSE Research Online Documents on Economics 108881, London School of Economics and Political Science, LSE Library.
    11. 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.

    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:oup:biomet:v:102:y:2015:i:4:p:753-766.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.