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.
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Software component provided by Boston College Department of Economics in its series Statistical Software Components with number
S456860.
Size: Programming language: Stata Requires: Stata version 9.2 Date of creation: 06 Apr 2006 Date of revision:
29 Nov 2006 Handle: RePEc:boc:bocode:s456860
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