Many real-world data sets possess features that are not adequately covered by academic research on large-dimensional covariance matrix estimation, such as:

- Time-series correlation across observations
- ARCH/GARCH effects
- Some very large eigenvalues
- Some eigenvalues very close to zero
- The case where the number of variables is equal to the sample size
- Need to estimate the correlation matrix instead of the covariance matrix
- Prior knowledge of patterns in the orientation of the eigenvectors of the covariance matrix
- Complex data
- Missing data
- Aggregation of class probability with case probability

and many other non-standard situations. Studdridge International delivers tailor-made solutions that fulfill the needs of the most demanding and sophisticated clients in the world.