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Reader Reaction: A note on testing and estimation in marker†set association study using semiparametric quantile regression kernel machine

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  • Xiang Zhan
  • Michael C. Wu

Abstract

Kong et al. (2016, Biometrics 72, 364–371) presented a quantile regression kernel machine (QRKM) test for robust analysis of genetic marker†set association studies. A potential limitation of QRKM is the permutation†based test design may be unscalable for the massive sizes of modern datasets. In this article, we present an alternative strategy for p†value calculation of QRKM, which is capable of speeding up the QRKM testing procedure dramatically while maintaining the same testing performance as QRKM. The effectiveness of our approach is demonstrated via simulation studies.

Suggested Citation

  • Xiang Zhan & Michael C. Wu, 2018. "Reader Reaction: A note on testing and estimation in marker†set association study using semiparametric quantile regression kernel machine," Biometrics, The International Biometric Society, vol. 74(2), pages 764-766, June.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:2:p:764-766
    DOI: 10.1111/biom.12785
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    References listed on IDEAS

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    1. Dehan Kong & Arnab Maity & Fang-Chi Hsu & Jung-Ying Tzeng, 2016. "Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine," Biometrics, The International Biometric Society, vol. 72(2), pages 364-371, June.
    2. Josse, J. & Pagès, J. & Husson, F., 2008. "Testing the significance of the RV coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 82-91, September.
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