IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v51y2007i8p3885-3897.html
   My bibliography  Save this article

Kernel estimation for adjusted p-values in multiple testing

Author

Listed:
  • Tsai, Chen-An
  • Chen, James J.

Abstract

No abstract is available for this item.

Suggested Citation

  • Tsai, Chen-An & Chen, James J., 2007. "Kernel estimation for adjusted p-values in multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3885-3897, May.
  • Handle: RePEc:eee:csdana:v:51:y:2007:i:8:p:3885-3897
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(06)00078-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    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. 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.

    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. Baolin Wu & Zhong Guan & Hongyu Zhao, 2006. "Parametric and Nonparametric FDR Estimation Revisited," Biometrics, The International Biometric Society, vol. 62(3), pages 735-744, September.
    2. Fu, Hsuan & Luger, Richard, 2022. "Multiple testing of the forward rate unbiasedness hypothesis across currencies," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 232-245.
    3. 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.
    4. Dazard, Jean-Eudes & Sunil Rao, J., 2012. "Joint adaptive mean–variance regularization and variance stabilization of high dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2317-2333.
    5. Yifan Gu & Yang Qi & Pulin Gong, 2019. "Rich-club connectivity, diverse population coupling, and dynamical activity patterns emerging from local cortical circuits," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-34, April.
    6. Sheng, Xuguang & Yang, Jingyun, 2013. "An adaptive truncated product method for combining dependent p-values," Economics Letters, Elsevier, vol. 119(2), pages 180-182.
    7. Pittelkow Yvonne E & Wilson Susan R, 2003. "Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 2(1), pages 1-19, September.
    8. Bergamelli, Michele & Bianchi, Annamaria & Khalaf, Lynda & Urga, Giovanni, 2019. "Combining p-values to test for multiple structural breaks in cointegrated regressions," Journal of Econometrics, Elsevier, vol. 211(2), pages 461-482.
    9. Lippmann, Quentin, 2022. "Gender and lawmaking in times of quotas," Journal of Public Economics, Elsevier, vol. 207(C).
    10. Zhenchuan Wang & Qiuying Sha & Shuanglin Zhang, 2016. "Joint Analysis of Multiple Traits Using "Optimal" Maximum Heritability Test," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    11. Richard Luger, 2024. "Regularizing stock return covariance matrices via multiple testing of correlations," Papers 2407.09696, arXiv.org.
    12. Bickel David R., 2008. "Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-27, March.
    13. Jesse Hemerik & Jelle Goeman, 2018. "Exact testing with random permutations," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(4), pages 811-825, December.
    14. Ebrahimi, Nader, 2008. "Simultaneous control of false positives and false negatives in multiple hypotheses testing," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 437-450, March.
    15. Kai Yu & William Wheeler & Qizhai Li & Andrew W. Bergen & Neil Caporaso & Nilanjan Chatterjee & Jinbo Chen, 2010. "A Partially Linear Tree-based Regression Model for Multivariate Outcomes," Biometrics, The International Biometric Society, vol. 66(1), pages 89-96, March.
    16. Edward L. Korn & Boris Freidlin, 2008. "A Note on Controlling the Number of False Positives," Biometrics, The International Biometric Society, vol. 64(1), pages 227-231, March.
    17. 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.
    18. Debashis Ghosh, 2006. "Shrunken p-Values for Assessing Differential Expression with Applications to Genomic Data Analysis," Biometrics, The International Biometric Society, vol. 62(4), pages 1099-1106, December.
    19. Ge, Yongchao & Sealfon, Stuart C. & Tseng, Chi-Hong & Speed, Terence P., 2007. "A Holm-type procedure controlling the false discovery rate," Statistics & Probability Letters, Elsevier, vol. 77(18), pages 1756-1762, December.
    20. Xuesong Yu & Timothy W. Randolph & Hua Tang & Li Hsu, 2010. "Detecting Genomic Aberrations Using Products in a Multiscale Analysis," Biometrics, The International Biometric Society, vol. 66(3), pages 684-693, September.

    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:eee:csdana:v:51:y:2007:i:8:p:3885-3897. 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/locate/csda .

    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.