Estimating the Distribution Function Under Di erent Sampling Schemes

Abstract

This study is based on estimating the population distribution function (DF) under di erent sampling schemes. We propose some classes of estimators using single and double auxiliary variables in estimating the DF. The proposed classes of estimators are extended to some other sampling schemes including simple random sampling and strati ed random sampling under non-response situations. The expressions for biases and mean squared errors are derived up to rst order of approximation. A numerical and simulation study is conducted to observe the performances of the estimators. The proposed classes of estimators are more e cient as compared to all other considered estimators.