Robust Lasso Regression with Student-t Residuals
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
Citar como
Statovic (2024). 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. Recuperado .
Compatibilidad con la versión de MATLAB
Compatibilidad con las plataformas
Windows macOS LinuxCategorías
- AI, Data Science, and Statistics > Statistics and Machine Learning Toolbox > Regression > Model Building and Assessment >
Etiquetas
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Descubra Live Editor
Cree scripts con código, salida y texto formateado en un documento ejecutable.
Versión | Publicado | Notas de la versión | |
---|---|---|---|
1.0.0.0 |