Efficiency of Novel Calibrated Variance Estimators under Random Non-Response in Successive Sampling

Abstract: This paper considers the problem of the estimation of population variance of the study character in two-occasion successive sampling in presence of non-response. A conventional estimator for estimating population variance is proposed for this situation. The conventional estimator was further modified using auxiliary variable information through calibration approach to reduce the nuisance effect of non-response in sample surveys. The proposed estimators aim to enhance the precision and validity of inferences drawn from successive sampling surveys, where non-sampling errors can significantly affect the quality of the data and the resulting population parameter estimates. The expressions for the mean squared errors (MSEs) and estimated MSEs of the proposed estimators were derived to quantify their statistical properties and performance. The empirical results through simulation studies demonstrated that the proposed estimators outperformed the proposed traditional variance estimator especially when non-response is substantial with exception of few cases.