Robust semiparametric inference for polytomous logistic regression with complex survey design

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Castilla González, Elena and Ghosh, Abhik and Martín Apaolaza, Nirian and Pardo Llorente, Leandro (2020) Robust semiparametric inference for polytomous logistic regression with complex survey design. Advances in Data Analysis and Classification . ISSN 1862-5347

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Official URL: https://doi.org/10.1007/s11634-020-00430-7




Abstract

Analyzing polytomous response from a complex survey scheme, like stratified or cluster sampling is very crucial in several socio-economics applications. We present a class of minimum quasi weighted density power divergence estimators for the polytomous logistic regression model with such a complex survey. This family of semiparametric estimators is a robust generalization of the maximum quasi weighted likelihood estimator exploiting the advantages of the popular density power divergence measure. Accordingly robust estimators for the design effects are also derived. Using the new estimators, robust testing of general linear hypotheses on the regression coefficients are proposed. Their asymptotic distributions and robustness properties are theoretically studied and also empirically validated through a numerical example and an extensive Monte Carlo study


Item Type:Article
Uncontrolled Keywords:Cluster sampling; Design effect; Minimum quasi weighted DPD estimator; Polytomous logistic regression model; Pseudo minimum phi-divergence estimator; Quasi-likelihood; Robustness
Palabras clave (otros idiomas):Regresión lineal
Subjects:Sciences > Statistics
Sciences > Statistics > Mathematical statistics
ID Code:63284
Deposited On:04 Dec 2020 09:14
Last Modified:19 Feb 2021 15:06

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