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M-regression, false discovery rates and outlier detection with application to genetic association studies

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  • Lourenço, V.M.
  • Pires, A.M.

Abstract

Robust multiple linear regression methods are valuable tools when underlying classical assumptions are not completely fulfilled. In this setting, robust methods ensure that the analysis is not significantly disturbed by any outlying observation. However, knowledge of these observations may be important to assess the underlying mechanisms of the data. Therefore, a robust outlier test is discussed, together with an adequate false discovery rate correction measure, to be used in the context of multiple linear regression with categorical explanatory variables. The methodology focuses on genetic association studies of quantitative traits, though it has much broader applications. The method is also compared to a benchmark rule from the literature and its good performance is validated by a simulation study and a real data example from a candidate gene study.

Suggested Citation

  • Lourenço, V.M. & Pires, A.M., 2014. "M-regression, false discovery rates and outlier detection with application to genetic association studies," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 33-42.
    • Handle: RePEc:eee:csdana:v:78:y:2014:i:c:p:33-42
    DOI: 10.1016/j.csda.2014.03.019
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    References listed on IDEAS

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    Cited by:

    1. Axel Gandy & Georg Hahn & Dong Ding, 2020. "Implementing Monte Carlo tests with p‐value buckets," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 950-967, September.

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