Alonso Revenga, Juana MaríaCalviño Martínez, AídaMuñoz López, SusanaBalakrishnan, NarayanaswamyGil, María ÁngelesMartín Apaolaza, NirianMorales González, DomingoPardo, María del Carmen2023-12-212023-12-212022Alonso, 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, 131-145.978-3-031-04137-22198-419010.1007/978-3-031-04137-2_13https://hdl.handle.net/20.500.14352/91716In 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.engbook parthttps://doi.org/10.1007/978-3-031-04137-2https://link.springer.com/book/10.1007/978-3-031-04137-2metadata only access519.2004.6Power DivergenceEstimatorHellinger DistanceEstadística1209.03 Análisis de Datos