Minimum Rényi pseudodistance estimators for logistic regression models

Alonso Revenga, Juana María; Calviño Martínez, Aída; Muñoz López, Susana

Minimum Rényi pseudodistance estimators for logistic regression models

Official URL

https://doi.org/10.1007/978-3-031-04137-2

Publication date

2022

Authors

Alonso Revenga, Juana María

Calviño Martínez, Aída

Muñoz López, Susana

Editors

Balakrishnan, Narayanaswamy

Gil, María Ángeles

Martín Apaolaza, Nirian

Morales González, Domingo

Pardo, María del Carmen

Publisher

Springer

Citations

Exportar

URI

https://hdl.handle.net/20.500.14352/91716

Citation

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.

Abstract

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.