Communications in Mathematics and Statistics https://doi.org/10.1007/s40304-018-00174-z An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling Ekaette I. Enang 1 · Etebong P. Clement 2 Received: 8 July 2017 / Revised: 16 November 2018 / Accepted: 27 December 2018 © School of Mathematical Sciences, University of Science and Technology of China and Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract This paper develops a new approach to domain estimation and proposes a new class of ratio estimators that is more efficient than the regression estimator and not depending on any optimality condition using the principle of calibration weightings. Some well- known regression and ratio-type estimators are obtained and shown to be special members of the new class of estimators. Results of analytical study showed that the new class of estimators is superior in both efficiency and biasedness to all related existing estimators under review. The relative performances of the new class of estimators with a corresponding global estimator were evaluated through a simulation study. Analysis and evaluation are presented. Keywords Auxiliary variable · Calibration approach · Efficiency · Global estimator · Ratio-type estimator · Stratified sampling · Study variable Mathematics Subject Classification 62D05 · 62G05 · 62H12 1 Introduction It is well known that the ratio and product estimators most practically have the limi- tation of having efficiency not exceeding that of the regression estimator. In the progression for better ratio (or product) estimators, authors like Singh and Vishwakarma [22], Sharma and Tailor [19], Onyeka [16], Singh and Audu [23], and Clement [2–4] have provided modifications to the existing ratio and product estima- B Etebong P. Clement epclement@yahoo.com Ekaette I. Enang ekkaass@yahoo.com 1 Department of Statistics, University of Calabar, Calabar, Nigeria 2 Department of Mathematics and Statistics, University of Uyo, Uyo, Nigeria 123