A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2014; you can also visit the original URL.
The file type is application/pdf
.
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso
2007
Journal of business & economic statistics
The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce LAD-lasso. Compared with the LAD regression, LAD-lasso can do parameter estimation and variable selection simultaneously. Compared with the traditional lasso, LAD-lasso is resistant to heavy-tailed errors
doi:10.1198/073500106000000251
fatcat:po5dng6h5fbkfhz4ltmbqvtoxq