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Reader Reaction: A note on testing and estimation in marker‐set association study using semiparametric quantile regression kernel machine
Author(s) -
Zhan Xiang,
Wu Michael C.
Publication year - 2018
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12785
Subject(s) - semiparametric regression , quantile regression , kernel (algebra) , econometrics , statistics , kernel regression , regression , quantile , semiparametric model , set (abstract data type) , estimation , mathematics , computer science , nonparametric statistics , economics , combinatorics , programming language , management
Summary 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.