# RegularizedSCM toolbox

The MATLAB toolbox RegularizedSCM is a collection of Matlab functions that can be used to compute different regularized sample covariance matrix (RSCM) estimators such as the Ell-RSCM estimators proposed by Ollila and Raninen (2019) or Ledoit and Wolf (2004).

The toolbox also contains functions and various examples on how the RSCM estimators can be utilized in various data analysis tasks such as classification based on regularized linear and quadratic discriminant analysis or portfolio optimization in finance using global minimum variance portfolio allocation.

We include demo examples on regularized discriminant analysis and portfolio optimization on real data sets which reproduce the results that were reported in Ollila and Raninen (2019).

## Contents

- How to cite
- Contact

## How to cite

If you use this toolbox or any of its function, please cite the software itself along with the publication:

- Esa Ollila and Elias Raninen, "Matlab RegularizedSCM Toolbox Version 1.0," Available online: html://http://users.spa.aalto.fi/esollila/regscm/, August 2018.
- Esa Ollila and Elias Raninen, "Optimal shrinkage covariance matrix estimation under random sampling from elliptical distributions," IEEE Transactions on Signal Processing, vol. 67, no. 10, pp. 2707 - 2719, 2019.

## Contact

Esa Ollila | Elias Raninen | Research group website
esa.ollila@aalto.fi | elias.raninen@aalto.fi | Toolbox website