Calibration based chain ratio-type estimator of population total under successive sampling

This article suggests a chain ratio-type estimator of population total based on calibration that takes into account auxiliary variables present on both occasions, and information on the study variable is not available on the first occasion. The optimal composite weights to choose, together with their performance range, are presented along with the bias expression. An empirical and simulation-based study is used to evaluate the effectiveness of the suggested estimator. The studies demonstrate that the proposed estimator outperforms the other estimators for various composite weight selections with varying matched and unmatched sample sizes.