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Estimating the False Discovery Rate Using Nonparametric Deconvolution

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  • Mark A. van de Wiel
  • Kyung In Kim

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  • Mark A. van de Wiel & Kyung In Kim, 2007. "Estimating the False Discovery Rate Using Nonparametric Deconvolution," Biometrics, The International Biometric Society, vol. 63(3), pages 806-815, September.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:3:p:806-815
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00736.x
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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. Margaret Sullivan Pepe & Gary Longton & Garnet L. Anderson & Michel Schummer, 2003. "Selecting Differentially Expressed Genes from Microarray Experiments," Biometrics, The International Biometric Society, vol. 59(1), pages 133-142, March.
    3. Kim‐Anh Do & Peter Müller & Feng Tang, 2005. "A Bayesian mixture model for differential gene expression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 627-644, June.
    4. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    5. A. Delaigle & I. Gijbels, 2002. "Estimation of integrated squared density derivatives from a contaminated sample," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 869-886, October.
    6. Mette Langaas & Bo Henry Lindqvist & Egil Ferkingstad, 2005. "Estimating the proportion of true null hypotheses, with application to DNA microarray data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(4), pages 555-572, September.
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    Cited by:

    1. J. Andrés Christen & Bruno Sansó & Mario Santana-Cibrian & Jorge X. Velasco-Hernández, 2016. "Bayesian deconvolution of oil well test data using Gaussian processes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(4), pages 721-737, March.
    2. Montazeri Zahra & Yanofsky Corey M. & Bickel David R., 2010. "Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-33, June.
    3. Alfo' Marco & Farcomeni Alessio & Tardella Luca, 2011. "A Three Component Latent Class Model for Robust Semiparametric Gene Discovery," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-19, January.
    4. Martin L. Hazelton & Berwin A. Turlach, 2010. "Semiparametric Density Deconvolution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 91-108, March.
    5. van Iterson Maarten & van de Wiel Mark A. & Boer Judith M. & de Menezes Renée X., 2013. "General power and sample size calculations for high-dimensional genomic data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 449-467, August.

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