RT Book, Section
T1 Minimum Rényi pseudodistance estimators for logistic regression models
A1 Alonso Revenga, Juana María
A1 Calviño Martínez, Aída
A1 Muñoz López, Susana
A2 Balakrishnan, Narayanaswamy
A2 Gil, María Ángeles
A2 Martín Apaolaza, Nirian
A2 Morales González, Domingo
A2 Pardo, María del Carmen
AB 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.
PB Springer
SN 978-3-031-04137-2
SN 2198-4190
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/91716
UL https://hdl.handle.net/20.500.14352/91716
LA eng
NO 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.
DS Docta Complutense
RD 21 ago 2024