RT Journal Article
T1 Robustness of Minimum Density Power Divergence Estimators and Wald-type test statistics in loglinear models with multinomial sampling
A1 Calviño Martínez, Aída
A1 Martín Apaolaza, Nirian
A1 Pardo Llorente, Leandro
A2 Brugnano, Luigi
A2 Efendiev, Yalchin
A2 Keller, André
AB In this paper we propose a new family of estimators, Minimum Density Power Divergence Estimators (MDPDE), as a robust generalization of maximum likelihood estimators (MLE) for the loglinear model with multinomial sampling by using the Density Power Divergence (DPD) measure introduced by Basu et al. (1998). Based on these estimators, we further develop two types of confidence intervals (asymptotic and bootstrap ones), as well as a new robust family of Wald-type test statistics for testing a nested sequence of loglinear models. Furthermore, we study theoretically the robust properties of both the MDPDE as well as Wald-type tests through the classical influence function analysis. Finally, a simulation study provides further confirmation of the validity of the theoretical results established in the paper.
SN 0377-0427
YR 2021
FD 2021
LK https://hdl.handle.net/20.500.14352/92189
UL https://hdl.handle.net/20.500.14352/92189
LA eng
NO Calviño, A., Martín, N., & Pardo, L. (2021). Robustness of minimum density power divergence estimators and wald-type test statistics in loglinear models with multinomial sampling. Journal of Computational and Applied Mathematics, 386
NO Ministerio de Ciencia, Innovación y Universidades (España)
DS Docta Complutense
RD 6 abr 2025