Kernel Machine Approach to Testing the Significance of Multiple Genetic Markers for Risk Prediction

DOI10.1111/j.1541-0420.2010.01544.xzbMath1226.62105OpenAlexW2136850689WikidataQ35190688 ScholiaQ35190688MaRDI QIDQ3100801

Xihong Lin, Giulia Tonini, Tianxi Cai

Publication date: 21 November 2011

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2010.01544.x

zbMATH Keywords

survival analysisscore testkernel PCAgenetic associationgene-set analysisgenetic pathways

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Testing in survival analysis and censored data (62N03)

Related Items

A Cross-Validated Ensemble Approach to Robust Hypothesis Testing of Continuous Nonlinear Interactions: Application to Nutrition-Environment Studies, Nonlinear predictive directions in clinical trials, Gene-level pharmacogenetic analysis on survival outcomes using gene-trait similarity regression, An asymptotically minimax kernel machine, Incorporating auxiliary information for improved prediction using combination of kernel machines, Testing error heterogeneity in censored linear regression, Omnibus Risk Assessment via Accelerated Failure Time Kernel Machine Modeling, Kernel machine testing for risk prediction with stratified case cohort studies, LCox: a tool for selecting genes related to survival outcomes using longitudinal gene expression data, FLCRM: Functional linear cox regression model, A regression perspective on generalized distance covariance and the Hilbert-Schmidt independence criterion

Cites Work

• Unnamed Item
• Unnamed Item
• U-processes: Rates of convergence
• Towards a general asymptotic theory for Cox model with staggered entry
• Score test of homogeneity for survival data. (With discussion)
• Semiparametric regression analysis for clustered failure time data
• Estimating subject-specific survival functions under the accelerated failure time model
• Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
• Testing Against a High Dimensional Alternative
• A resampling method based on pivotal estimating functions
• Radial Basis Functions
• Approximate Inference in Generalized Linear Mixed Models
• Semiparametric Regression of Multidimensional Genetic Pathway Data: Least‐Squares Kernel Machines and Linear Mixed Models
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines