IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v88y2020is1ps60-s63.html
   My bibliography  Save this article

Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron

Author

Listed:
  • Emmanuel Candès
  • Chiara Sabatti

Abstract

No abstract is available for this item.

Suggested Citation

  • Emmanuel Candès & Chiara Sabatti, 2020. "Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 60-63, December.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:s1:p:s60-s63
    DOI: 10.1111/insr.12412
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/insr.12412
    Download Restriction: no
    File URL: https://libkey.io/10.1111/insr.12412?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. M Sesia & C Sabatti & E J Candès, 2019. "Rejoinder: ‘Gene hunting with hidden Markov model knockoffs’," Biometrika, Biometrika Trust, vol. 106(1), pages 35-45.
    2. Matteo Sesia & Eugene Katsevich & Stephen Bates & Emmanuel Candès & Chiara Sabatti, 2020. "Multi-resolution localization of causal variants across the genome," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    3. M Sesia & C Sabatti & E J Candès, 2019. "Gene hunting with hidden Markov model knockoffs," Biometrika, Biometrika Trust, vol. 106(1), pages 1-18.
    4. Matteo Sesia & Eugene Katsevich & Stephen Bates & Emmanuel Candès & Chiara Sabatti, 2020. "Publisher Correction: Multi-resolution localization of causal variants across the genome," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    5. Emmanuel Candès & Yingying Fan & Lucas Janson & Jinchi Lv, 2018. "Panning for gold: ‘model‐X’ knockoffs for high dimensional controlled variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 551-577, June.
    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. Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    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. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
    4. Panxu Yuan & Yinfei Kong & Gaorong Li, 2024. "FDR control and power analysis for high-dimensional logistic regression via StabKoff," Statistical Papers, Springer, vol. 65(5), pages 2719-2749, July.
    5. Ruth Heller, 2020. "Comments on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 51-55, March.
    6. L Bottolo & S Richardson, 2019. "Discussion of ‘Gene hunting with hidden Markov model knockoffs’," Biometrika, Biometrika Trust, vol. 106(1), pages 19-22.
    7. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    8. Subhadeep Mukhopadhyay, 2021. "InfoGram and Admissible Machine Learning," Papers 2108.07380, arXiv.org, revised Aug 2021.
    9. 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).
    10. D García Rasines & G A Young, 2023. "Splitting strategies for post-selection inference," Biometrika, Biometrika Trust, vol. 110(3), pages 597-614.
    11. Pedro Delicado & Daniel Peña, 2023. "Understanding complex predictive models with ghost variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 107-145, March.
    12. Shi, Chengchun & Xu, Tianlin & Bergsma, Wicher & Li, Lexin, 2021. "Double generative adversarial networks for conditional independence testing," LSE Research Online Documents on Economics 112550, London School of Economics and Political Science, LSE Library.
    13. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    14. Winn-Nuñez, Emily T. & Griffin, Maryclare & Crawford, Lorin, 2024. "A simple approach for local and global variable importance in nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    15. Dong, Yan & Li, Daoji & Zheng, Zemin & Zhou, Jia, 2022. "Reproducible feature selection in high-dimensional accelerated failure time models," Statistics & Probability Letters, Elsevier, vol. 181(C).
    16. N. Hernández & J. Soenksen & P. Newcombe & M. Sandhu & I. Barroso & C. Wallace & J. L. Asimit, 2021. "The flashfm approach for fine-mapping multiple quantitative traits," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    17. Rajchert, Andrew & Keich, Uri, 2023. "Controlling the false discovery rate via competition: Is the +1 needed?," Statistics & Probability Letters, Elsevier, vol. 197(C).
    18. Pan, Yingli, 2022. "Feature screening and FDR control with knockoff features for ultrahigh-dimensional right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    19. Yumei Ren & Guoqiang Tang & Xin Li & Xuchang Chen, 2023. "A Study of Multifactor Quantitative Stock-Selection Strategies Incorporating Knockoff and Elastic Net-Logistic Regression," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    20. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.

    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:istatr:v:88:y:2020:i:s1:p:s60-s63. 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/isiiinl.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.