AN ALTERNATIVE APPROACH TO SOLVE THE LAD-LASSO PROBLEM
SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI, vol.34, no.3, pp.467-476, 2016 (ESCI)
- Publication Type: Article / Article
- Volume: 34 Issue: 3
- Publication Date: 2016
- Journal Name: SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI
- Journal Indexes: Emerging Sources Citation Index (ESCI), Academic Search Premier, Directory of Open Access Journals
- Page Numbers: pp.467-476
- Istanbul University Affiliated: Yes
Abstract
The least absolute deviation (LAD) regression is more robust alternative to the popular least squares (LS) regression whenever there are outliers in the response variable, or the errors follow a heavy-tailed distribution. The least absolute shrinkage and selection operator (LASSO) is a popular choice for shrinkage estimation and variable selection. By combining these two classical ideas, LAD-LASSO is an estimator which is able to perform shrinkage estimation while at the same time selecting the variables and is resistant to heavy-tailed distributions and outliers. The aim of this article is to reformulate LAD-LASSO problem to solve with the Simplex Algorithm, which is an area of Mathematical Programming.