Summary Of Lasso Variable Selection Methods

Variable selection is one of the important steps in the modeling process.Choosing the right variable can greatly simplify the model complexity and improve the interpretability of the model.This article summarizes Lasso-type penalty methods,including Lasso,Adaptive Lasso,SCAD and other derived methods,Elastics Net,Group Lasso,Fused Lasso,Graph Lasso and other extended methods,focusing on their statistical properties and advantages,summarizing the relevant algorithms and the relevant application of each method.The article concludes with a brief list of other variable selection methods and discusses the latest developments and challenges.