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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/215131
Alternative Functionals Estimation Based on Index Models and Kernel Approach
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This work explores the utilization of double cross-validation methods for determining the optimal bandwidth in kernel regression using single index-models. Kernel regression is a non-parametric technique widely employed in various fields, particularly in smoothing noisy data. The bandwidth parameter plays a crucial role in kernel regression, controlling the smoothness of the estimated function. Selecting an appropriate bandwidth is essential for achieving accurate and robust model performance. Traditional approaches to band- width selection often rely on heuristic methods or fixed rules, which may not be optimal for all datasets (...)
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Treballs Finals del Màster de Ciències Actuarials i Financeres, Facultat d'Economia i Empresa, Universitat de Barcelona, Curs: 2023-2024, Tutoria: Catalina Bolancé Losilla
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DAI, Lei. Alternative Functionals Estimation Based on Index Models and Kernel Approach. [consulted: 12 of June of 2026]. Available at: https://hdl.handle.net/2445/215131