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oai:docta.ucm.es:20.500.14352/917162025-03-12T15:05:14Zcom_20.500.14352_14col_20.500.14352_18

Minimum Rényi pseudodistance estimators for logistic regression models Alonso Revenga, Juana María Calviño Martínez, Aída Muñoz López, Susana Balakrishnan, Narayanaswamy Gil, María Ángeles Martín Apaolaza, Nirian Morales González, Domingo Pardo, María del Carmen In this work we propose a new family of estimators, called minimum Rényi pseudodistance estimators (MRPE), as a robust generalization of maximum likelihood estimators (MLE) for the logistic regression model based on the Rényi pseudodistance introduced by Jones et al. [14], along with their corresponding asymptotic distribution. Based on this information, we further develop three types of confidence intervals (approximate and parametric and non-parametric bootstrap ones). Finally, a simulation study is conducted considering different levels of outliers, where a better behavior of the MRPE with respect to the MLE is shown. 2023-12-21T13:34:14Z 2023-12-21T13:34:14Z 2022 book part Alonso, J. M., Calviño, A., & Muñoz, S. (2022). Minimum Rényi Pseudodistance Estimators for Logistic Regression Models. Trends in Mathematical, Information and Data Sciences: A Tribute to Leandro Pardo, 131-145. 978-3-031-04137-2 2198-4190 10.1007/978-3-031-04137-2_13 https://hdl.handle.net/20.500.14352/91716 https://doi.org/10.1007/978-3-031-04137-2_13 https://link.springer.com/book/10.1007/978-3-031-04137-2 eng Studies in systems, decision and control metadata only access Springer