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Testing and estimation in marker‐set association study using semiparametric quantile regression kernel machine
Author(s) -
Kong Dehan,
Maity Arnab,
Hsu FangChi,
Tzeng JungYing
Publication year - 2016
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.12438
Subject(s) - quantile regression , semiparametric regression , kernel (algebra) , kernel regression , quantile , computer science , statistics , regression , estimation , kernel method , semiparametric model , kernel smoother , econometrics , mathematics , artificial intelligence , radial basis function kernel , nonparametric statistics , support vector machine , economics , management , combinatorics
Summary We consider quantile regression for partially linear models where an outcome of interest is related to covariates and a marker set (e.g., gene or pathway). The covariate effects are modeled parametrically and the marker set effect of multiple loci is modeled using kernel machine. We propose an efficient algorithm to solve the corresponding optimization problem for estimating the effects of covariates and also introduce a powerful test for detecting the overall effect of the marker set. Our test is motivated by traditional score test, and borrows the idea of permutation test. Our estimation and testing procedures are evaluated numerically and applied to assess genetic association of change in fasting homocysteine level using the Vitamin Intervention for Stroke Prevention Trial data.