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Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model

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  • Lee, Donghwan
  • Lee, Youngjo

Abstract

Current multiple testing procedures are often based on assumptions of independence of observations. However, the observations in genomics and neuroimaging are correlated and ignoring such a correlation can severely distort the conclusions of a test. Moreover, most tests investigate two-sided alternatives only as a two-action problem and do not worry about directional errors. Misspecifications in signs of effects should not be regarded as power. In this study, we derive an optimal multiple testing procedure to incorporate dependence among tests, controlling directional false discovery rates. Real data examples for gene expression and neuroimaging using hidden Markov random field models show that an appropriate model is crucial for the efficiency of tests. Proper modeling of the correlation structure and model selection tools in the likelihood approach enhance the performance of a test. Reporting the estimates of various error rates is useful for the test’s validity.

Suggested Citation

  • Lee, Donghwan & Lee, Youngjo, 2016. "Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model," Journal of Multivariate Analysis, Elsevier, vol. 151(C), pages 1-13.
    • Handle: RePEc:eee:jmvana:v:151:y:2016:i:c:p:1-13
    DOI: 10.1016/j.jmva.2016.07.001
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    References listed on IDEAS

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    1. Sun, Wenguang & Cai, T. Tony, 2007. "Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 901-912, September.
    2. Youngjo Lee & Jan F. Bjørnstad, 2013. "Extended likelihood approach to large-scale multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 553-575, June.
    3. Wenguang Sun & T. Tony Cai, 2009. "Large‐scale multiple testing under dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 393-424, April.
    4. Efron, Bradley, 2007. "Correlation and Large-Scale Simultaneous Significance Testing," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 93-103, March.
    5. Yoav Benjamini & Daniel Yekutieli, 2005. "False Discovery Rate-Adjusted Multiple Confidence Intervals for Selected Parameters," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 71-81, March.
    6. 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.
    7. Alessio Farcomeni, 2007. "Some Results on the Control of the False Discovery Rate under Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 275-297, June.
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