regMMD: An R package for parametric estimation and regression with maximum mean discrepancy

Fuente: arXiv
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Hauptverfasser: Alquier, Pierre, Gerber, Mathieu
Format: Preprint
Veröffentlicht: 2025
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author Alquier, Pierre
Gerber, Mathieu
author_facet Alquier, Pierre
Gerber, Mathieu
contents The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including various regression models, within an R package called regMMD. This paper provides an introduction to the regMMD package. We describe the available kernels and optimization procedures, as well as the default settings. Detailed applications to simulated and real data are provided.
format Preprint
id arxiv_https___arxiv_org_abs_2503_05297
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle regMMD: An R package for parametric estimation and regression with maximum mean discrepancy
Alquier, Pierre
Gerber, Mathieu
Computation
Methodology
The Maximum Mean Discrepancy (MMD) is a kernel-based metric widely used for nonparametric tests and estimation. Recently, it has also been studied as an objective function for parametric estimation, as it has been shown to yield robust estimators. We have implemented MMD minimization for parameter inference in a wide range of statistical models, including various regression models, within an R package called regMMD. This paper provides an introduction to the regMMD package. We describe the available kernels and optimization procedures, as well as the default settings. Detailed applications to simulated and real data are provided.
title regMMD: An R package for parametric estimation and regression with maximum mean discrepancy
topic Computation
Methodology
url https://arxiv.org/abs/2503.05297