one, are based on the quasi maximum likelihood approach.

Even though the maximum quasi weighted likelihood estimator is the base of most of the existing literature on logistic models under complex survey designs, it is known to be non-robust in the presence of possible outliers in the data. In practice, with such a complex survey design, it is quite natural to have some outlying observations that make the likelihood based inference highly unstable. So, we often may need to make additional efforts to find and discard the outliers from the data before their analyses. A robust method providing stable solution even in presence of the outliers will be really helpful and more efficient in practice. The cited work by Castilla et al (2018) has developed an alternative minimum divergence estimator based on $\phi $-divergences (Pardo, 2006), but the important issue of robustness is still ignored there.

In this paper, we develop a robust estimator under the PLR model with a complex survey based on a minimum quasi weighted divergence approach. In particular, we exploit the nice properties of the density power divergence (DPD) of Basu et al. (1998). This measure become very popular in recent literature for yielding highly robust 