A Two-Stage Regularization Method for Variable Selection and Forecasting in High-Order Interaction Model
Forecasting models with high-order interaction has become popular in many applications since researchers gradually notice that an additive linear model is not adequate for accurate forecasting. However, the excessive number of variables with low sample size in the model poses critically challenges t...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/2032987 |
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