Subset Selection with Shrinkage: Sparse Linear Modeling When the SNR Is Low
<jats:p> Learning Compact High-Dimensional Models in Noisy Environments </jats:p><jats:p> Building compact, interpretable statistical models where the output depends upon a small number of input features is a well-known problem in modern analytics applications. A fundamental tool u...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute for Operations Research and the Management Sciences (INFORMS)
2022
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| Online Access: | https://hdl.handle.net/1721.1/144220 |