[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v63y2011i6p1141-1163.html
   My bibliography  Save this article

An optimal approach for hypothesis testing in the presence of incomplete data

Author

Listed:
  • Albert Vexler
  • Sergey Tarima
Abstract
No abstract is available for this item.

Suggested Citation

  • Albert Vexler & Sergey Tarima, 2011. "An optimal approach for hypothesis testing in the presence of incomplete data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(6), pages 1141-1163, December.
    • Handle: RePEc:spr:aistmt:v:63:y:2011:i:6:p:1141-1163
    DOI: 10.1007/s10463-010-0270-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10463-010-0270-0
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10463-010-0270-0?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. Jelle J. Goeman & Sara A. Van De Geer & Hans C. Van Houwelingen, 2006. "Testing against a high dimensional alternative," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 477-493, June.
    2. Krieger A.M. & Pollak M. & Yakir B., 2003. "Surveillance of a Simple Linear Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 456-469, January.
    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. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
    2. Zihuai He & Min Zhang & Seunggeun Lee & Jennifer A. Smith & Sharon L. R. Kardia & V. Diez Roux & Bhramar Mukherjee, 2017. "Set-Based Tests for the Gene–Environment Interaction in Longitudinal Studies," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 966-978, July.
    3. Bin Guo & Song Xi Chen, 2016. "Tests for high dimensional generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(5), pages 1079-1102, November.
    4. Moshe Pollak & Michal Shauly-Aharonov, 2019. "A Double Recursion for Calculating Moments of the Truncated Normal Distribution and its Connection to Change Detection," Methodology and Computing in Applied Probability, Springer, vol. 21(3), pages 889-906, September.
    5. 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.
    6. Albert Vexler & Chengqing Wu & Kai Yu, 2010. "Optimal hypothesis testing: from semi to fully Bayes factors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(2), pages 125-138, March.
    7. Frisén, Marianne, 2008. "Introduction to financial surveillance," Research Reports 2008:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    8. Rui Wang & Xingzhong Xu, 2021. "A Bayesian-motivated test for high-dimensional linear regression models with fixed design matrix," Statistical Papers, Springer, vol. 62(4), pages 1821-1852, August.
    9. Ma, Yingying & Lan, Wei & Wang, Hansheng, 2015. "Testing predictor significance with ultra high dimensional multivariate responses," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 275-286.
    10. Lebrec Jeremie J & Stijnen Theo & van Houwelingen Hans C, 2010. "Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-22, January.
    11. Jesse Hemerik & Jelle J. Goeman & Livio Finos, 2020. "Robust testing in generalized linear models by sign flipping score contributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 841-864, July.
    12. Konstantin Glombek, 2014. "Statistical Inference for High-Dimensional Global Minimum Variance Portfolios," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 845-865, December.
    13. Zang, Yangguang & Zhang, Sanguo & Li, Qizhai & Zhang, Qingzhao, 2016. "Jackknife empirical likelihood test for high-dimensional regression coefficients," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 302-316.
    14. Hong Guo & Changliang Zou & Zhaojun Wang & Bin Chen, 2014. "Empirical likelihood for high-dimensional linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 921-945, October.
    15. Peter Jensen, 2010. "Testing the null of a low dimensional growth model," Empirical Economics, Springer, vol. 38(1), pages 193-215, February.
    16. de Lima, Max Sousa & Duczmal, Luiz Henrique, 2014. "Adaptive likelihood ratio approaches for the detection of space–time disease clusters," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 352-370.
    17. Ningning Xu & Aldo Solari & Jelle J. Goeman, 2023. "Closed testing with Globaltest, with application in metabolomics," Biometrics, The International Biometric Society, vol. 79(2), pages 1103-1113, June.
    18. Jiang Dandan & Sun Jianguo, 2017. "Group Tests for High-dimensional Failure Time Data with the Additive Hazards Models," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-10, May.
    19. Lan, Wei & Zhong, Ping-Shou & Li, Runze & Wang, Hansheng & Tsai, Chih-Ling, 2016. "Testing a single regression coefficient in high dimensional linear models," Journal of Econometrics, Elsevier, vol. 195(1), pages 154-168.
    20. Liu, Yang & Sun, Wei & Hsu, Li & He, Qianchuan, 2022. "Statistical inference for high-dimensional pathway analysis with multiple responses," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).

    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:aistmt:v:63:y:2011:i:6:p:1141-1163. 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.