RT info:eu-repo/semantics/article
T1 Robust weighted regression via PAELLA sample weights
T2 Regresión ponderada robusta usando los pesos muestrales de PAELLA
A1 Castejón Limas, Manuel
A1 Alaiz Moretón, Héctor
A1 Fernández Robles, Laura
A1 Sánchez González, Lidia
A1 Fernández Llamas, Camino
A1 Pérez García, Hilde
A2 Proyectos de Ingenieria
K1 Estadística
K1 Regresión ponderada
K1 Espurios
K1 Outliers
K1 6105.04 Estadística
AB [EN] This paper reports the usage of the occurrence vector provided by the PAELLA algorithm in the context of robust regression. PAELLA was originally conceived as an outlier detection and data cleaning technique. A novel approach is to use this algorithm not for discarding outliers but to generate information related to the reliability of the observations recorded in the dataset. This approach proves to provide successful results when compared to traditional common practice such as outlier removal. A set of experiments using a contrived difficult artificial dataset are described using both neural networks and classical polynomial fitting. Finally, a successful comparison of our approach to two state-of-the-art algorithms proves the benefits of using the PAELLA algorithm in the context of robust regression.
PB Elsevier
SN 0925-2312
LK https://hdl.handle.net/10612/18224
UL https://hdl.handle.net/10612/18224
NO Castejón-Limas, M., Alaiz-Moreton, H., Fernández-Robles, L., Alfonso-Cendón, J., Fernández-Llamas, C., Sánchez-González, L., & Pérez, H. (2020). Robust weighted regression via PAELLA sample weights. Neurocomputing, 391, 325–333. doi:10.1016/j.neucom.2019.03.108
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 20-may-2024