regMMD - Robust Regression and Estimation Through Maximum Mean
Discrepancy Minimization
The functions in this package compute robust estimators by
minimizing a kernel-based distance known as MMD (Maximum Mean
Discrepancy) between the sample and a statistical model. Recent
works proved that these estimators enjoy a universal
consistency property, and are extremely robust to outliers.
Various optimization algorithms are implemented: stochastic
gradient is available for most models, but the package also
allows gradient descent in a few models for which an exact
formula is available for the gradient. In terms of distribution
fit, a large number of continuous and discrete distributions
are available: Gaussian, exponential, uniform, gamma, Poisson,
geometric, etc. In terms of regression, the models available
are: linear, logistic, gamma, beta and Poisson. Alquier, P. and
Gerber, M. (2024) <doi:10.1093/biomet/asad031>
Cherief-Abdellatif, B.-E. and Alquier, P. (2022)
<doi:10.3150/21-BEJ1338>.
