2026-08-20T15:41:05Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1056282024-07-04T23:58:17Zcom_20.500.14352_14col_20.500.14352_15

Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators Jaenada Malagón, María Pardo Llorente, Leandro MDPI Generalized linear model Independent and nonidentically distributed observations Minimum Rényi’s pseudodistance estimators Robust Wald-type test statistics for GLMs Influence function for GLMs Poisson regression model Estadística matemática (Matemáticas) 1209 Estadística 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. Ministerio de Innovación, Ciencia y Universidades Depto. de Estadística e Investigación Operativa Fac. de Ciencias Matemáticas TRUE pub 2024-07-04T13:26:23Z 2024-07-04T13:26:23Z 2022 journal article https://hdl.handle.net/20.500.14352/105628 1099-4300 10.3390/e24010123 eng info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-095194-B-I00/ES/ESTIMACION Y CONTRASTES DE HIPOTESIS ROBUSTOS PARA DATOS DE ALTA DIMENSION BASADOS EN LA DIVERGENCIA DE POTENCIAS DE DENSIDAD/ Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ open access application/pdf MDPI