RT Journal Article
T1 Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators
A1 Jaenada Malagón, María
A1 Pardo Llorente, Leandro
A2 MDPI, 
AB Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.
PB MDPI
SN 1099-4300
YR 2022
FD 2022
LK https://hdl.handle.net/20.500.14352/105628
UL https://hdl.handle.net/20.500.14352/105628
LA eng
NO Ministerio de Innovación, Ciencia y Universidades
DS Docta Complutense
RD 7 abr 2025