Identification and estimation of interaction effects in nonparametric additive regression
A new formulation of the additive interaction model is introduced. In contrast to existing approaches, the new formulation separates well the joint effects of covariates that cannot be accounted for by individual main effects. The new approach enables correct interpretation of interaction effects by...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2026
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| In: |
Biometrika
Year: 2026, Volume: 113, Issue: 1, Pages: 1-21 |
| ISSN: | 1464-3510 |
| DOI: | 10.1093/biomet/asaf074 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1093/biomet/asaf074
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| Author Notes: | by Seung Hyun Moon, Byeong U Park, Enno Mammen and Young Kyung Lee |
| Summary: | A new formulation of the additive interaction model is introduced. In contrast to existing approaches, the new formulation separates well the joint effects of covariates that cannot be accounted for by individual main effects. The new approach enables correct interpretation of interaction effects by making them orthogonal to the associated main effects in the $ L^{2} $ sense. A new method is developed to estimate the resulting main and interaction effects. Asymptotic $ L^{2} $ error rates are derived for the estimators under mild technical conditions. Numerical evidence is provided via simulation studies and real-data examples. |
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| Item Description: | Online veröffentlicht: 31 October 2025 Gesehen am 18.05.2026 |
| Physical Description: | Online Resource |
| ISSN: | 1464-3510 |
| DOI: | 10.1093/biomet/asaf074 |