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Robustness of Minimum Density Power Divergence Estimators and Wald-type test statistics in loglinear models with multinomial sampling Calviño Martínez, Aída Martín Apaolaza, Nirian Pardo Llorente, Leandro Brugnano, Luigi Efendiev, Yalchin Keller, André 519.243 Point estimation MLE Confidence intervals Bootstrap Influence function Monte Carlo simulation Muestreo (Estadística) 1209.10 Teoría y Técnicas de Muestreo 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. Ministerio de Ciencia, Innovación y Universidades (España) Depto. de Estadística y Ciencia de los Datos Fac. de Estudios Estadísticos TRUE pub 2024-01-10T10:10:38Z 2024-01-10T10:10:38Z 2021 journal article AM https://hdl.handle.net/20.500.14352/92189 0377-0427 10.1016/j.cam.2020.113214 eng PGC2018-095194-B-I00 from 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 Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ open access application/pdf