Robust lasso regression with Student-t residuals

I have recently uploaded some new MATLAB code that implements lasso based estimation of linear models in which the residuals follow a Student-t distibution using the expectation-maximisation algorithm. By varying the degrees-of-freedom parameter of the Student-t likelihood, the model can be made more resistant to outlying observations.

The software has the following features:

  1. Automatically generate complete lasso regularisation paths for a given degrees-of-freedom
  2. Selection of lasso regularisation parameter and degrees-of-freedom using either cross-validation or information criteria.

The code is straightforward to run, efficient and comes with several examples that recreate the analyses from the paper below. To cite this toolbox, please use the reference below:

The code can be obtained from MathWorks File Exchange. If you find this code useful, I would be greatly obliged if you could leave a comment or rating on the above File Exchange page.



  1. “Robust Lasso Regression with Student-t Residuals”, D. F. Schmidt and E. Makalic, Lecture Notes in Artificial Intelligence, to appear, 2016


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