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Alonso Revenga, Juana María Calviño Martínez, Aída Muñoz López, Susana 2023-12-21T13:34:14Z 2023-12-21T13:34:14Z 2022 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 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. eng metadata only access Minimum Rényi pseudodistance estimators for logistic regression models book part