summary.estMMD

Summary method for the <code>class</code> <code>"estMMD"</code>

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>.

Pierre Alquier

regMMD

Robust Regression and Estimation Through Maximum Mean
Discrepancy Minimization

Mathieu Gerber

summary.estMMD function

<dl><dt>object</dt>
<dd>An object of <code>class</code> <code>"estMMD"</code>.</dd>
<dt>...</dt>
<dd>Additional arguments (not used).</dd></dl>

Arguments

Summary method for the class "estMMD" — summary.estMMD

<dl>

<dt>object</dt>
<dd>An object of <code>class</code> <code>"estMMD"</code>.</dd>


<dt>...</dt>
<dd>Additional arguments (not used).</dd>

</dl>

summary.estMMD: Summary method for the `class` `"estMMD"`

Description

Usage

Value

Arguments