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Bivariate high-level exceedance and the Chen–Stein theorem in genomics multiple hypothesis testing perspectives

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  • Sen, Pranab K.
  • Kang, Moonsu

Abstract

In genomic studies, generally the genes are neither independent nor marginally identically distributed, though in microarray studies and DNA/RNA SNP models, often they are assumed to be independent and identically distributed. A version of the Chen–Stein theorem on Poisson approximation for dependent binary variables has been adopted for a mathematical justification of this approach in a general genomic setup.

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  • Sen, Pranab K. & Kang, Moonsu, 2013. "Bivariate high-level exceedance and the Chen–Stein theorem in genomics multiple hypothesis testing perspectives," Statistics & Probability Letters, Elsevier, vol. 83(7), pages 1725-1730.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:7:p:1725-1730
    DOI: 10.1016/j.spl.2013.03.019
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    References listed on IDEAS

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    1. Sen, Pranab K. & Tsai, Ming-Tien & Jou, Yuh-Shan, 2007. "High-Dimension, LowSample Size Perspectives in Constrained Statistical Inference: The SARSCoV RNA Genome in Illustration," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 686-694, June.
    2. Masaaki Sibuya, 1959. "Bivariate extreme statistics, I," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 11(2), pages 195-210, June.
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    Cited by:

    1. Daniel Fischer & Hannu Oja & Johanna Schleutker & Pranab K. Sen & Tiina Wahlfors, 2014. "Generalized Mann–Whitney Type Tests for Microarray Experiments," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 672-692, September.

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