Semiparametric kernel-based regression for evaluating interaction between pathway effect and covariate

DOI10.1007/s13253-017-0317-2zbMath1391.62270OpenAlexW2771932837MaRDI QIDQ1654561

Zaili Fang, Jeesun Jung, Inyoung Kim

Publication date: 8 August 2018

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-017-0317-2

zbMATH Keywords

smoothing splinessemiparametric modelkernel machineGaussian random processpathway analysis

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to environmental and related topics (62P12)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Generalization of likelihood ratio tests under nonstandard conditions
• Statistical properties on semiparametric regression for evaluating pathway effects
• Bayesian semiparametric model for pathway-based analysis with zero-inflated clinical outcomes
• Some results on Tchebycheffian spline functions and stochastic processes
• Powerful Tests for Detecting a Gene Effect in the Presence of Possible Gene-Gene Interactions Using Garrote Kernel Machines
• Variance component testing in generalised linear models with random effects
• A Direct Approach to False Discovery Rates
• Restricted Likelihood Ratio Lack-of-Fit Tests Using Mixed Spline Models
• Exact likelihood ratio tests for penalised splines
• Flexible variable selection for recovering sparsity in nonadditive nonparametric models
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
• Hypothesis testing in semiparametric additive mixed models
• Theory of Reproducing Kernels

This page was built for publication: Semiparametric kernel-based regression for evaluating interaction between pathway effect and covariate