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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 519.2 004.6 Power Divergence Estimator Hellinger Distance Estadística 1209.03 Análisis de Datos 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. Depto. de Estadística y Ciencia de los Datos Fac. de Estudios Estadísticos TRUE pub 2023-12-21T13:34:14Z 2023-12-21T13:34:14Z 2022 book part https://hdl.handle.net/20.500.14352/91716 2198-4190 10.1007/978-3-031-04137-2_13 eng Studies in systems, decision and control 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. metadata only access Springer