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Robust group testing for multiple traits with misclassification

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  • Juan Ding
  • Wenjun Xiong

Abstract

Determining group size is a crucial stage before conducting experiments using group testing methods. Considering misclassification, we propose D -criterion and A -criterion to determine a robust group size for screening multiple infections simultaneously. Extensive simulation shows the advantage of the proposed method when the goal is estimation.

Suggested Citation

  • Juan Ding & Wenjun Xiong, 2015. "Robust group testing for multiple traits with misclassification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(10), pages 2115-2125, October.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:10:p:2115-2125
    DOI: 10.1080/02664763.2015.1019841
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    References listed on IDEAS

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    1. Hae-Young Kim & Michael G. Hudgens & Jonathan M. Dreyfuss & Daniel J. Westreich & Christopher D. Pilcher, 2007. "Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error," Biometrics, The International Biometric Society, vol. 63(4), pages 1152-1163, December.
    2. Joshua M. Tebbs & Christopher S. McMahan & Christopher R. Bilder, 2013. "Two-Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility Prevention Project," Biometrics, The International Biometric Society, vol. 69(4), pages 1064-1073, December.
    3. Joshua M. Tebbs, 2003. "Estimating ordered binomial proportions with the use of group testing," Biometrika, Biometrika Trust, vol. 90(2), pages 471-477, June.
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    Cited by:

    1. Gregory Haber & Yaakov Malinovsky, 2020. "On the Construction of Unbiased Estimators for the Group Testing Problem," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 220-241, February.

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