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

Xiang Zhan, Michael C. Wu

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)764-766
Number of pages3
JournalBiometrics
Volume74
Issue number2
DOIs
StatePublished - Jun 1 2018

Fingerprint

Kernel Machines
Semiparametric Regression
Quantile Regression
Genetic Markers
Testing
Biometrics
seeds
testing
p-Value
biometry
Permutation
Datasets
Simulation Study
genetic markers
Alternatives

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

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Reader Reaction : A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine. / Zhan, Xiang; Wu, Michael C.

In: Biometrics, Vol. 74, No. 2, 01.06.2018, p. 764-766.

Research output: Contribution to journalArticle

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