This code implements the estimation of robust regression models using the lasso procedure. Robustness is handled by modelling the residuals as arising from a Student-t distribution with an appropriate degrees-of-freedom. The optimization is performed using the expectation-maximization algorithm.
Primary features of the code:
* Automatically produce a complete lasso regularization path for a given degrees-of-freedom
* Select amount of regularization, and the degrees-of-freedom using cross-validation or information criteria
To cite this toolbox:
Schmidt, D.F. and Makalic, E.
Robust Lasso Regression with Student-t Residuals
Lecture Notes in Artificial Intelligence, to appear, 2017
Statovic (2021). Robust Lasso Regression with Student-t Residuals (https://www.mathworks.com/matlabcentral/fileexchange/63037-robust-lasso-regression-with-student-t-residuals), MATLAB Central File Exchange. Retrieved .
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