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A Decision-Theory Approach to Interpretable Set Analysis for High-Dimensional Data

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  • Simina M. Boca
  • Héctor Céorrada Bravo
  • Brian Caffo
  • Jeffrey T. Leek
  • Giovanni Parmigiani

Abstract

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Suggested Citation

  • Simina M. Boca & Héctor Céorrada Bravo & Brian Caffo & Jeffrey T. Leek & Giovanni Parmigiani, 2013. "A Decision-Theory Approach to Interpretable Set Analysis for High-Dimensional Data," Biometrics, The International Biometric Society, vol. 69(3), pages 614-623, September.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:3:p:614-623
    DOI: 10.1111/biom.12060
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    References listed on IDEAS

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    1. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    2. Cai, T. Tony & Sun, Wenguang, 2009. "Simultaneous Testing of Grouped Hypotheses: Finding Needles in Multiple Haystacks," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1467-1481.
    3. Christopher Genovese & Larry Wasserman, 2002. "Operating characteristics and extensions of the false discovery rate procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 499-517, August.
    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.
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    1. Amruta Nori-Sarma & Anobha Gurung & Gulrez Shah Azhar & Ajit Rajiva & Dileep Mavalankar & Perry Sheffield & Michelle L. Bell, 2017. "Opportunities and Challenges in Public Health Data Collection in Southern Asia: Examples from Western India and Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 9(7), pages 1-9, June.

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