Abstract
Linearly combining the elements of a vector of covariates to get a scalar-valued feature is common practice in regression modelling. In this work, we propose a novel approach to integrate single index effects in Generalised Additive Models (GAMs). In particular, model fitting and inference are performed by exploiting the efficient methods proposed in [7]. We consider an application to daily electricity load consumption data, demonstrating improved predictive performance relative to traditional GAMs. This integrated approach provides a valuable tool to capture complex relationships in real-world applications, while preserving interpretability.
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Acknowledgments
Claudia Collarin PhD scholarship is funded by PON “Research and Innovation” 2014 – 2020 Action IV.5 “PhDs on Green issues.” – Ministerial Decree 1061/2021. Matteo Fasiolo’s work has been partially funded by EDF R &D.
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Collarin, C., Fasiolo, M. (2024). Integrating Single Index Effects in Generalized Additive Models. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling. IWSM 2024. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-65723-8_18
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DOI: https://doi.org/10.1007/978-3-031-65723-8_18
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