A Logistic Regression Analysis Approach for Sample Survey Data Based on Phi-Divergence Measures

A new family of minimum distance estimators for binary logistic regression models based on ϕ$$\phi $$-divergence measures is introduced. The so called “pseudo minimum phi-divergence estimator”(PMϕ$$\phi $$E) family is presented as an extension of “minimum phi-divergence estimator” (Mϕ$$\phi $$E) for general sample survey designs and contains, as a particular case, the pseudo maximum likelihood estimator (PMLE) considered in Roberts et al. (Biometrika 74:1–12, [8]). Through a simulation study it is shown that some PMϕ$$\phi $$Es have a better behaviour, in terms of efficiency, than the PMLE.