LARS stands for Least Angle Regression. It is a feature selection technique for multiple regression that incorporates a penalty.
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Using LASSO from lars (or glmnet) package in R for variable selection
Sorry if this question comes across a little basic.
I am looking to use LASSO variable selection for a multiple linear regression model in R. I have 15 predictors, one of which is categorical(will ...
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Least angle regression packages for R or MATLAB
I am looking for a Least Angle Regression (LAR) packages in R or MATLAB which can be used for classification problems.
The only package that I currently know which fits this description is glmpath. ...
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1answer
81 views
'Forward Stagewise' option in LARS algorithm
Can anyone help me understand the forward stagewise part in the LARS algorithm? I was reading the R code and could not figure out what is ...
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1answer
79 views
Max steps in lars
I don't know how many steps are necessary for lars() to select the variables till the algorithm proceeds to the saturated fit (especially using the ...
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Constant signs of correlation in the active set in least angle regression
I am trying to comprehend the proof of the Least Angle Regression algorithm and I am stuck at certain points. I would appreciate any help that I can get.
Let me set the stage:
I am following the ...
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1answer
175 views
LARS - LASSO with weights
I am interested in solving the following problem
$$ \min_{\boldsymbol{\beta}} \left( \mathbf{y}-\mathbf{X}\boldsymbol{\beta} \right)^T W \left( \mathbf{y}-\mathbf{X}\boldsymbol{\beta} \right) + ...
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Advantages of doing “double lasso” or performing lasso twice?
I once heard a method of using the lasso twice (like a double-lasso) where you perform lasso on the original set of variables, say S1, obtain a sparse set called S2, and then perform lasso again on ...
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Rolling window using LARS [closed]
I have written some code for a rolling window in matlab for ridge regression. The code uses a 30 day window to learn the data and outputs a lamda for the least error. I am using financial time series ...
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1answer
255 views
LASSO/LARS vs general to specific (GETS) method
I have been wondering, why are LASSO and LARS model selection methods so popular even though they are basically just variations of step-wise forward selection (and thus suffer from path dependency)?
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946 views
What problem do shrinkage methods solve?
The holiday season has given me the opportunity to curl up next to the fire with The Elements of Statistical Learning. Coming from a (frequentist) econometrics perspective, I'm having trouble grasping ...
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211 views
Feature selection with k-fold cross-validated least angle regression
I am using the least angle regression (LARS) to extract the most important predictors ($x_1, x_2,...,x_p$) for my response variable ($y$).
I have seven predictors ($x_1,x_2,...,x_7$) for each ...
