References
==========

The MVP documentation cites the following robust-statistics and covariance-estimation background.
The implementation is intentionally pragmatic and experimental; these references provide the
mathematical context rather than a claim that every estimator is a line-by-line reproduction of a
specific paper.

Robust covariance and MCD
-------------------------

* P. J. Rousseeuw. 1984. Least median of squares regression. *Journal of the American Statistical Association*.
* P. J. Rousseeuw. 1985. Multivariate estimation with high breakdown point. In *Mathematical Statistics and Applications*.
* P. J. Rousseeuw and K. Van Driessen. 1999. A fast algorithm for the minimum covariance determinant estimator. *Technometrics*.
* M. Hubert, M. Debruyne, and P. J. Rousseeuw. 2018. Minimum covariance determinant and extensions. *WIREs Computational Statistics*.

Tyler and robust scatter M-estimation
-------------------------------------

* D. E. Tyler. 1987. A distribution-free M-estimator of multivariate scatter. *The Annals of Statistics*.
* R. A. Maronna. 1976. Robust M-estimators of multivariate location and scatter. *The Annals of Statistics*.
* E. Ollila, D. E. Tyler, V. Koivunen, and H. V. Poor. 2012. Complex elliptically symmetric distributions: survey, new results and applications. *IEEE Transactions on Signal Processing*.

Regularization and shrinkage
----------------------------

* O. Ledoit and M. Wolf. 2004. A well-conditioned estimator for large-dimensional covariance matrices. *Journal of Multivariate Analysis*.
* Y. Chen, A. Wiesel, and A. O. Hero. 2011. Robust shrinkage estimation of high-dimensional covariance matrices. *IEEE Transactions on Signal Processing*.
* A. Wiesel. 2012. Unified framework to regularized covariance estimation in scaled Gaussian models. *IEEE Transactions on Signal Processing*.
* Y. Sun, P. Babu, and D. P. Palomar. 2014. Regularized Tyler's scatter estimator: existence, uniqueness, and algorithms. *IEEE Transactions on Signal Processing*.


Matrix geometry and geodesic convexity
--------------------------------------

* R. Bhatia. 2007. *Positive Definite Matrices*. Princeton University Press.
* S. Sra and R. Hosseini. 2015. Conic geometric optimization on the manifold of positive definite matrices. *SIAM Journal on Optimization*.
* M. Moakher. 2005. A differential geometric approach to the geometric mean of symmetric positive-definite matrices. *SIAM Journal on Matrix Analysis and Applications*.

Heavy-tailed covariance and Student-t models
--------------------------------------------

* K. L. Lange, R. J. A. Little, and J. M. G. Taylor. 1989. Robust statistical modeling using the t distribution. *Journal of the American Statistical Association*.
* G. McLachlan and T. Krishnan. 2008. *The EM Algorithm and Extensions*. Wiley.
* R. A. Maronna, R. D. Martin, V. J. Yohai, and M. Salibián-Barrera. 2019. *Robust Statistics: Theory and Methods*. Wiley.

Robust anomaly diagnostics
--------------------------

* P. J. Rousseeuw and A. M. Leroy. 1987. *Robust Regression and Outlier Detection*. Wiley.
* M. Hubert, P. J. Rousseeuw, and K. Vanden Branden. 2005. ROBPCA: a new approach to robust principal component analysis. *Technometrics*.


Robust clustering and mixtures
------------------------------

* A. C. Atkinson and M. Riani. 2000. *Robust Diagnostic Regression Analysis*. Springer.
* A. García-Escudero, A. Gordaliza, C. Matrán, and A. Mayo-Iscar. 2008. A general trimming approach to robust cluster analysis. *The Annals of Statistics*.
* G. J. McLachlan and D. Peel. 2000. *Finite Mixture Models*. Wiley.
* G. J. McLachlan and D. Peel. 1998/2000. Robust cluster analysis via mixtures of multivariate t-distributions. Related robust mixture-model literature.