LARS: Stata module to perform least angle regression
Least Angle Regression is a model-building algorithm that considers parsimony as well as prediction accuracy. This method is covered in detail by the paper Efron, Hastie, Johnstone and Tibshirani (2004), published in The Annals of Statistics. Their motivation for this method was a computationally simpler algorithm for the Lasso and Forward Stagewise regression. There are many criticisms of stepwise regression, one of which is that it is a "greedy" algorithm and that the regression coefficients are too large. Ridge regression is one method of model-building that shrinks the coefficients by making the sum of the squared coefficients less than some constant. The Lasso is similar but the constaint is that the sum of the "mod" coefficients is less than a constant. One implication of this will be that the solution will contain coefficients that are exactly 0 and hence have the property of parsimony i.e. a simpler model.
least angle regression, lasso, forward stagewise regression, model-building, parsimony
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