A logistic regression analysis approach for sample survey data based on phi-divergence measures

Abstract

A new family of minimum distance estimators for binary logistic regression models based on $\varphi$-divergence measures is introduced. The so called "pseudo minimum phi-divergence estimator"(PM$\varphi$E) family is presented as an extension of "minimum phi-divergence estimator" (M$\varphi$E) for general sample survey designs and contains, as a particular case, the pseudo maximum likelihood estimator (PMLE) considered in Roberts et al. \cite{r}. Through a simulation study it is shown that some PM$\varphi$Es have a better behaviour, in terms of efficiency, than the PMLE.