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2015 | OriginalPaper | Chapter

A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis

Authors : Olga Krasotkina, Vadim Mottl

Published in: Machine Learning and Data Mining in Pattern Recognition

Publisher: Springer International Publishing

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Abstract

Survival prediction and prognostic factor identification play an important role in machine learning research. This paper employs the machine learning regression algorithms for constructing survival model. The paper suggests a new Bayesian framework for feature selection in high-dimensional Cox regression problems. The proposed approach gives a strong probabilistic statement of the shrinkage criterion for feature selection. The proposed regularization gives the estimates that are unbiased, possesses grouping and oracle properties, their maximal risk diverges to a finite value. Experimental results show that the proposed framework is competitive on both simulated data and publicly available real data sets.

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Appendix
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Metadata
Title
A Bayesian Approach to Sparse Cox Regression in High-Dimentional Survival Analysis
Authors
Olga Krasotkina
Vadim Mottl
Copyright Year
2015
Book
Machine Learning and Data Mining in Pattern Recognition

Print ISBN: 978-3-319-21023-0

Electronic ISBN: 978-3-319-21024-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-21024-7_30

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