AI RESEARCH
Linear Models, Variable Selection, Artificial Intelligence
arXiv CS.LG
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ArXi:2604.27191v1 Announce Type: cross 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.