2025/03/07 by Pierre Alquier, Alquier, Pierre, Mathieu Gerber +1 · 1 voice
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #Statistical Methods and Inference
paper · doi:10.48550/arxiv.2503.05297
openalex publication_date 2025/03/07 · openalex created_date 2025/10/18 · openalex updated_date 2026/08/01
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.