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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.16894 |
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| _version_ | 1866915943542685696 |
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| author | Hasegawa, Hiroki Tamura, Aoba Okada, Yukihiko |
| author_facet | Hasegawa, Hiroki Tamura, Aoba Okada, Yukihiko |
| contents | Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings with $p>n$. To address this, we propose a novel estimation principle that reformulates the covariance structure into self-covariance and cross-covariance components. The resulting framework defines a likelihood-based feasible set combined with a relative error constraint, enabling stable estimation in small-sample settings where $p>n$ for sign and direction. Experiments on synthetic and real-world data show improved stability, particularly in recovering the sign and direction of structural parameters. These results extend covariance-based SEM to small-sample settings and provide practically useful directional information for decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_16894 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$ Hasegawa, Hiroki Tamura, Aoba Okada, Yukihiko Machine Learning Methodology Factor-based Structural Equation Modeling (SEM) relies on likelihood-based estimation assuming a nonsingular sample covariance matrix, which breaks down in small-sample settings with $p>n$. To address this, we propose a novel estimation principle that reformulates the covariance structure into self-covariance and cross-covariance components. The resulting framework defines a likelihood-based feasible set combined with a relative error constraint, enabling stable estimation in small-sample settings where $p>n$ for sign and direction. Experiments on synthetic and real-world data show improved stability, particularly in recovering the sign and direction of structural parameters. These results extend covariance-based SEM to small-sample settings and provide practically useful directional information for decision-making. |
| title | Covariance-Based Structural Equation Modeling in Small-Sample Settings with $p>n$ |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2604.16894 |