IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v21y2012i2p280-300.html
   My bibliography  Save this article

Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed

Author

Listed:
  • Hironori Fujisawa
  • Takayuki Sakaguchi

Abstract

No abstract is available for this item.

Suggested Citation

  • Hironori Fujisawa & Takayuki Sakaguchi, 2012. "Optimal significance analysis of microarray data in a class of tests whose null statistic can be constructed," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 280-300, June.
  • Handle: RePEc:spr:testjl:v:21:y:2012:i:2:p:280-300
    DOI: 10.1007/s11749-011-0243-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11749-011-0243-5
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11749-011-0243-5?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. Raphael Gottardo & Adrian E. Raftery & Ka Yee Yeung & Roger E. Bumgarner, 2006. "Bayesian Robust Inference for Differential Gene Expression in Microarrays with Multiple Samples," Biometrics, The International Biometric Society, vol. 62(1), pages 10-18, March.
    2. repec:bla:biomet:v:62:y:2006:i:1:p:10-18:2 is not listed on IDEAS
    3. Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
    4. 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.
    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. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    2. 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.
    3. Cipolli III, William & Hanson, Timothy & McLain, Alexander C., 2016. "Bayesian nonparametric multiple testing," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 64-79.
    4. 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.
    5. Shigeyuki Matsui & Shu Zeng & Takeharu Yamanaka & John Shaughnessy, 2008. "Sample Size Calculations Based on Ranking and Selection in Microarray Experiments," Biometrics, The International Biometric Society, vol. 64(1), pages 217-226, March.
    6. Alessio Farcomeni, 2006. "More Powerful Control of the False Discovery Rate Under Dependence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(1), pages 43-73, May.
    7. Nik Tuzov & Frederi Viens, 2011. "Mutual fund performance: false discoveries, bias, and power," Annals of Finance, Springer, vol. 7(2), pages 137-169, May.
    8. Leek Jeffrey T & Storey John D., 2011. "The Joint Null Criterion for Multiple Hypothesis Tests," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-22, June.
    9. Ishwaran, Hemant & Sunil Rao, J., 2008. "Clustering gene expression profile data by selective shrinkage," Statistics & Probability Letters, Elsevier, vol. 78(12), pages 1490-1497, September.
    10. Kenneth Rice & David Spiegelhalter, 2006. "A Simple Diagnostic Plot Connecting Robust Estimation, Outlier Detection, and False Discovery Rates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1131-1147.
    11. Andrew Y. Chen, 2022. "Most claimed statistical findings in cross-sectional return predictability are likely true," Papers 2206.15365, arXiv.org, revised Sep 2024.
    12. Friguet, Chloé & Causeur, David, 2011. "Estimation of the proportion of true null hypotheses in high-dimensional data under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2665-2676, September.
    13. 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.
    14. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    15. Patrick Kline & Christopher Walters, 2021. "Reasonable Doubt: Experimental Detection of Job‐Level Employment Discrimination," Econometrica, Econometric Society, vol. 89(2), pages 765-792, March.
    16. Patrick Kline & Evan K Rose & Christopher R Walters, 2022. "Systemic Discrimination Among Large U.S. Employers [“Teachers and Student Achievement in the Chicago Public High Schools,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 1963-2036.
    17. Bickel David R., 2013. "Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 529-543, August.
    18. Joshua Habiger & Edsel Peña, 2011. "Randomised -values and nonparametric procedures in multiple testing," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 583-604.
    19. N. Bochkina & S. Richardson, 2007. "Tail Posterior Probability for Inference in Pairwise and Multiclass Gene Expression Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1117-1125, December.
    20. 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.

    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:spr:testjl:v:21:y:2012:i:2:p:280-300. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.