Linear Models, Variable Selection, Artificial Intelligence
Variable selection in linear regression models has been a problem since hypothesis testing began. Which variables to include or exclude from a model is not an easy task. Techniques such as Forward, Back ward, Stepwise Regression sequentially add or delete variables from a model. Penalized likelihood methods such as AIC, BIC, etc. seek to choose variables that have a significant contribution to the likelihood. Penalized sum of square methods such as LASSO and Elastic Net have been used to penalize small coefficients to only allow variables with large coefficients in the model. This work introduces an Artificial Intelligence approach to model selection where an ANN is trained to determine t...
| Authors | By Riyadh Alrawkan, Edward Boone, Ryad Ghanam et al. |
|---|---|
| Primary category | stat.ME |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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