IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v118y2016icp8-15.html
   My bibliography  Save this article

Demystifying the bias from selective inference: A revisit to Dawid’s treatment selection problem

Author

Listed:
  • Lu, Jiannan
  • Deng, Alex

Abstract

We extend the heuristic discussion in Senn (2008) on the bias from selective inference for the treatment selection problem (Dawid, 1994), by deriving the closed-form expression for the selection bias. We illustrate the advantages of our theoretical results through numerical and simulated examples.

Suggested Citation

  • Lu, Jiannan & Deng, Alex, 2016. "Demystifying the bias from selective inference: A revisit to Dawid’s treatment selection problem," Statistics & Probability Letters, Elsevier, vol. 118(C), pages 8-15.
    • Handle: RePEc:eee:stapro:v:118:y:2016:i:c:p:8-15
    DOI: 10.1016/j.spl.2016.06.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715216300888
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2016.06.007?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
    ---><---

    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. Senn, Stephen, 2008. "A Note Concerning a Selection Paradox of Dawid's," The American Statistician, American Statistical Association, vol. 62, pages 206-210, August.
    2. Max Grazier G'Sell & Stefan Wager & Alexandra Chouldechova & Robert Tibshirani, 2016. "Sequential selection procedures and false discovery rate control," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 423-444, March.
    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. Damian Kozbur, 2020. "Analysis of Testing‐Based Forward Model Selection," Econometrica, Econometric Society, vol. 88(5), pages 2147-2173, September.
    2. Stanley, T. D. & Doucouliagos, Hristos, 2011. "Meta-regression approximations to reduce publication selection bias," Working Papers eco_2011_4, Deakin University, Department of Economics.
    3. Gong, Siliang & Zhang, Kai & Liu, Yufeng, 2018. "Efficient test-based variable selection for high-dimensional linear models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 17-31.
    4. Li Gengxin & Cui Yuehua & Zhao Hongyu, 2015. "An Empirical Bayes risk prediction model using multiple traits for sequencing data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(6), pages 551-573, December.
    5. Weijie J Su, 2018. "When is the first spurious variable selected by sequential regression procedures?," Biometrika, Biometrika Trust, vol. 105(3), pages 517-527.
    6. David R. Bickel, 2014. "Small-scale Inference: Empirical Bayes and Confidence Methods for as Few as a Single Comparison," International Statistical Review, International Statistical Institute, vol. 82(3), pages 457-476, December.
    7. Jeng, X. Jessie & Chen, Xiongzhi, 2019. "Predictor ranking and false discovery proportion control in high-dimensional regression," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 163-175.
    8. X. Jessie Jeng & Huimin Peng & Wenbin Lu, 2021. "Model Selection With Mixed Variables on the Lasso Path," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 170-184, May.
    9. Andrew Y Chen & Tom Zimmermann & Jeffrey Pontiff, 2020. "Publication Bias and the Cross-Section of Stock Returns," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(2), pages 249-289.
    10. Xu Zhao & Zhongxian Zhang & Weihu Cheng & Pengyue Zhang, 2019. "A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework," Mathematics, MDPI, vol. 7(5), pages 1-18, May.
    11. Vitor Pessoa Colombo & Jérôme Chenal & Brama Koné & Martí Bosch & Jürg Utzinger, 2022. "Using Open-Access Data to Explore Relations between Urban Landscapes and Diarrhoeal Diseases in Côte d’Ivoire," IJERPH, MDPI, vol. 19(13), pages 1-20, June.
    12. Yu Wang & Ling Wang & Jianhua Zong & Dongxiao Lv & Shumao Wang, 2021. "Research on Loading Method of Tractor PTO Based on Dynamic Load Spectrum," Agriculture, MDPI, vol. 11(10), pages 1-14, October.
    13. Dallakyan, Aramayis & Kim, Rakheon & Pourahmadi, Mohsen, 2022. "Time series graphical lasso and sparse VAR estimation," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    14. Yang, Chiao-Yu & Lei, Lihua & Ho, Nhat & Fithian, William, 2022. "BONuS: Multiple Multivariate Testing with a Data-Adaptive Test Statistic," Research Papers 4031, Stanford University, Graduate School of Business.
    15. Pengfei Wang & Wensheng Zhu, 2022. "Large‐scale covariate‐assisted two‐sample inference under dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1421-1447, December.
    16. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    17. Dylan Troop & Frédéric Godin & Jia Yuan Yu, 2022. "Best-Arm Identification Using Extreme Value Theory Estimates of the CVaR," JRFM, MDPI, vol. 15(4), pages 1-15, April.
    18. Markus Pelger & Jiacheng Zou, 2022. "Inference for Large Panel Data with Many Covariates," Papers 2301.00292, arXiv.org, revised Mar 2023.
    19. Shiyun Chen & Ery Arias-Castro, 2021. "On the power of some sequential multiple testing procedures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 311-336, April.

    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:eee:stapro:v:118:y:2016:i:c:p:8-15. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.