RT Journal Article
T1 Model Selection for independent not identically distributed observations based on Rényi's pseudodistances
A1 Felipe Ortega, Ángel
A1 Jaenada Malagón, María
A1 Miranda Menéndez, Pedro
A1 Pardo Llorente, Leandro
AB Model selection criteria are rules used to select the best statistical model among a set of candidate models, striking a trade-off between goodness of fit and model complexity. Most popular model selection criteria measure the goodness of fit trough the model log-likelihood function, yielding to non-robust criteria. This paper presents a new family of robust model selection criteria for independent but not identically distributed observations (i.n.i.d.o.) based on the Rényi's pseudodistance (RP). The RP-based model selection criterion is indexed with a tuning parameter α controlling the trade-off between efficiency and robustness. Some theoretical results about the RP criterion are derived and the theory is applied to the multiple linear regression model, obtaining explicit expressions of the model selection criterion. Moreover, restricted models are considered and explicit expressions under the multiple linear regression model with nested models are accordingly derived. Finally, a simulation study empirically illustrates the robustness advantage of the method.
YR 2023
FD 2023-04-11
LK https://hdl.handle.net/20.500.14352/73280
UL https://hdl.handle.net/20.500.14352/73280
LA eng
NO Felipe A, Jaenada M, Miranda P, Pardo L. Model Selection for independent not identically distributed observations based on Rényi’s pseudodistances. Journal of Computational and Applied Mathematics 2024; 440: 115630. [DOI: 10.1016/j.cam.2023.115630]
NO Ministerio de Ciencia e Innovación (España) 
DS Docta Complutense
RD 9 jun 2025