RT Journal Article
T1 Robust semiparametric inference for polytomous logisticregression with complex survey design
A1 Castilla González, Elena María
A1 Ghosh, Abhik
A1 Martín Apaolaza, Nirian
A1 Pardo Llorente, Leandro
AB 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
PB Springer
SN 1862-5347
YR 2020
FD 2020-11-23
LK https://hdl.handle.net/20.500.14352/7580
UL https://hdl.handle.net/20.500.14352/7580
LA eng
NO Ministerio de Ciencia e Innovación (MICINN)
DS Docta Complutense
RD 6 oct 2024