https://github.com/citiususc/survivalrkhs
Model-free Variable Selection in Reproducing Kernel Hilbert Space for right-censored data
https://github.com/citiususc/survivalrkhs
Last synced: about 1 year ago
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Model-free Variable Selection in Reproducing Kernel Hilbert Space for right-censored data
- Host: GitHub
- URL: https://github.com/citiususc/survivalrkhs
- Owner: citiususc
- Created: 2019-12-02T17:50:12.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-02T18:35:15.000Z (over 6 years ago)
- Last Synced: 2025-01-29T19:23:54.287Z (over 1 year ago)
- Language: C++
- Size: 15.6 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# survivalRKHS
Model-free Variable Selection in Reproducing Kernel Hilbert Space for right-censored data
M. Matabuena and P. Félix and C. Meixide-Garcı́a
Abstract. A novel variable selection method is presented for those
sort of regression problems in survival analysis where the response
variable can be right-censored. The proposed approach is model-free,
that is, no model assumption is made between the response and pre-
dictors, and is implemented through a two-stage procedure: firstly, a
previous independence screening is performed to efficiently reduce
dimensionality in high-dimensional data sets; and secondly, from the
remaining variables, the informative predictors are identified as those
showing a gradient function substantially different from zero. Both
stages are formulated in a learning framework based on Reproducing
Kernel Hilbert Space (RKHS). The effectiveness of this method is
supported experimentally by multiple synthetic data sets and a real
data set from survival in cancer patients, proving that the new method
outperforms classical methods in scenarios with non-linear relation-
ships between variables.