Probing for Sparse and Fast Variable Selection with Model-Based Boosting
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slig...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2017-01-01
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| Series: | Computational and Mathematical Methods in Medicine |
| Online Access: | http://dx.doi.org/10.1155/2017/1421409 |