Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R
Abstract
Stein's method constructs computable discrepancies between a target distribution and a candidate distribution without requiring the target distribution's normalizing constant. These discrepancies support goodness-of-fit tests for model assessment as well as sampling tools for empirical approximation. The R package steinsampling provides the first unified R workflow for applying score-based Stein methods to kernel goodness-of-fit testing of independent or serially dependent observations, point transport, greedy point construction, and sample compression. High-level functions carry out each task in a single call, while the kernel, calibration, optimization, and transition components are provided separately so that users can replace any one of them. A single score and kernel setup can therefore be reused across sampling and testing, making these methods easier to reproduce, compare, and extend.
Related
- Conditional Kernel Stein Discrepancy
- Goodness-of-Fit Tests via Kernel Density Estimation: Univariate and Multivariate Approaches
- Portmanteau Goodness-of-Fit Tests in the Presence of Simple, Mixed, and Multiple Seasonal Autocorrelation
- Entropy-Based Goodness-of-Fit Tests for Generalized κ -Gaussian Distributions
- Bivariate Frank Copula: Some More Results on Point Estimation of the Association Parameter from a Bayesian Perspective and Revisiting the Goodness of Fit Tests with an Application to Model Groundwater Data from Dong Thap, Vietnam
BibTeX
@misc{gao2026kernelized,
title = {{Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R}},
author = {Junhao Gao and Ery Arias-Castro},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.26450},
eprint = {2608.26450},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.26450},
}
RIS
TY - UNPB
TI - Kernelized Stein Discrepancy for Goodness-of-Fit Tests and Stein Sampling in R
AU - Junhao Gao
AU - Ery Arias-Castro
PY - 2026
DA - 2026/10/11
PB - arXiv
DO - 10.48550/arxiv.2608.26450
UR - https://arxiv.org/abs/2608.26450
AB - Stein's method constructs computable discrepancies between a target distribution and a candidate distribution without requiring the target distribution's normalizing constant. These discrepancies support goodness-of-fit tests for model assessment as well as sampling tools for empirical approximation. The R package steinsampling provides the first unified R workflow for applying score-based Stein methods to kernel goodness-of-fit testing of independent or serially dependent observations, point transport, greedy point construction, and sample compression. High-level functions carry out each task in a single call, while the kernel, calibration, optimization, and transition components are provided separately so that users can replace any one of them. A single score and kernel setup can therefore be reused across sampling and testing, making these methods easier to reproduce, compare, and extend.
ER -