Testing and estimation in marker-set association study using semiparametric quantile regression kernel machine
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Updated: Mar 30, 2026

Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Dehan Kong1, Arnab Maity2, Fang-Chi Hsu3
1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, U.S.A.
Abstract:
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.
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