Pathwise least angle regression and a significance test for the elastic net

@article{Tabassum2017PathwiseLA,
  title={Pathwise least angle regression and a significance test for the elastic net},
  author={Muhammad Naveed Tabassum and Esa Ollila},
  journal={2017 25th European Signal Processing Conference (EUSIPCO)},
  year={2017},
  pages={1309-1313}
}
Least angle regression (LARS) by Efron et al. (2004) is a novel method for constructing the piece-wise linear path of Lasso solutions. For several years, it remained also as the de facto method for computing the Lasso solution before more sophisticated optimization algorithms preceded it. LARS method has recently again increased its popularity due to its ability to find the values of the penalty parameters, called knots, at which a new parameter enters the active set of non-zero coefficients… 
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[Least Angle Regression]: Discussion