The geometry of Stein's method of moments: A canonical decomposition via score matching
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911512183963648 |
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| author | Nagai, Mitsuki Yano, Keisuke |
| author_facet | Nagai, Mitsuki Yano, Keisuke |
| contents | In this paper, we elucidate the geometry of Stein's method of moments (SMoM). SMoM is a parameter estimation method based on the Stein operator, and yields a wide class of estimators that do not depend on the normalizing constant. We present a canonical decomposition of an SMoM estimator after centering the score matching estimator, which sheds light on the central role of the score matching within the SMoM framework. Using this decomposition, we construct an SMoM estimator that improves upon the score matching estimator in the asymptotic variance. We also discuss the connection between SMoM and the Wasserstein geometry. Specifically, using the Wasserstein score function, we provide a geometrical interpretation of the gap in the asymptotic variance between the score matching estimator and the maximum likelihood estimator. Furthermore, it is shown that the score matching estimator is asymptotically efficient if and only if the Fisher score functions span the same space as the Wasserstein score functions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_12843 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The geometry of Stein's method of moments: A canonical decomposition via score matching Nagai, Mitsuki Yano, Keisuke Statistics Theory Methodology In this paper, we elucidate the geometry of Stein's method of moments (SMoM). SMoM is a parameter estimation method based on the Stein operator, and yields a wide class of estimators that do not depend on the normalizing constant. We present a canonical decomposition of an SMoM estimator after centering the score matching estimator, which sheds light on the central role of the score matching within the SMoM framework. Using this decomposition, we construct an SMoM estimator that improves upon the score matching estimator in the asymptotic variance. We also discuss the connection between SMoM and the Wasserstein geometry. Specifically, using the Wasserstein score function, we provide a geometrical interpretation of the gap in the asymptotic variance between the score matching estimator and the maximum likelihood estimator. Furthermore, it is shown that the score matching estimator is asymptotically efficient if and only if the Fisher score functions span the same space as the Wasserstein score functions. |
| title | The geometry of Stein's method of moments: A canonical decomposition via score matching |
| topic | Statistics Theory Methodology |
| url | https://arxiv.org/abs/2603.12843 |