2025/11/17 by Pierre Alquier, Alquier, Pierre, Mathieu Gerber +1
Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #FOS: Mathematics #Statistical Methods and Bayesian Inference #Statistical Methods and Inference
paper · doi:10.57750/d6d1-gb09
openalex publication_date 2025/11/17 · openalex created_date 2025/11/19 · openalex updated_date 2026/07/08
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.