An Exponential Ratio Type Estimator of the Population Mean in The Presence of Non-Response Using Double Sampling

In modern era, proper and effective planning can be only possible using statistical techniques to estimate different characteristics of population under studies. An appropriate sample design based on efficient estimation technique is desirable to extract maximum information from sample data. It is a well-known phenomenon to use auxiliary information and to reduce the negative impact of non-response using Hansen & Hurwitz approach that further increase the efficiency of an estimator. Information on one or more auxiliary variables correlated with study variable in several ways to get more reliable estimate. The current paper presents a novel Exponential ratio type estimator to estimate the population mean under the problem of non- response. The proposed estimator further reduces the mean square error in the case of double sampling scheme. Approximate algebraic expressions of the mean square error are discussed; in addition, two real applications are also presented. Several ratio and regression type estimators were developed which perform better with several optimization constants under double sampling in the presence of non-response. However, the proposed estimator has utilized information on auxiliary variables in exponential form and place optimization constants in such positions which further increase the efficiency of the estimator even in all level of correlation coefficients among study and auxiliary variables. Real world data examples, as well as simulation study have been performed to know efficiency of the proposed method over mentioned competitors.