Censored Data Regression in High‐Dimensional and Low‐Sample‐Size Settings for Genomic Applications
Wiley series in probability and statisticsPublished 23 March 2007
Hongzhe Li
Citations15
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TL;DR
Some recently developed methods for censored data regression in the high-dimension and low-sample size setting, with emphasis on applications to genomic data are reviewed.
Abstract
This chapter contains sections titled: Introduction Censored Data Regression Models Regularized Estimation for Censored Data Regression Models Survival Ensemble Methods Nonparametric-Pathway-Based Regression Models Dimension-Reduction-Based Methods and Bayesian Variable Selection Methods Criteria for Evaluating Different Procedures Application to a Real Dataset and Comparisons Discussion and Future Research Topics Concluding Remarks
Keywords
Biochemistry, Genetics and Molecular Biology
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