Estimation Through Calibration Under Stratified Sampling with Non-Response and Measurement Error Effects

In survey sampling, the presence of non-response and measurement error often leads to biased and inefficient estimates, particularly in stratified random sampling designs. This study introduces a new calibration estimation technique for stratified sampling that effectively accounts for non-response and measurement error. By incorporating auxiliary data and optimizing calibrated weights, the proposed estimator minimizes bias and enhances efficiency. The estimator employs auxiliary information through calibrated weights derived using a chi-square-type distance function. Furthermore, the performance of the suggested calibration estimator has been compared with that of the Hansen and Hurwitz’ estimator, the separate ratio-type estimator and the Singh’s estimator. To validate the efficiency and superiority of the proposed method over traditional estimators, an empirical evaluation has been carried out using simulated datasets. The comparative assessment with existing estimators demonstrates that the proposed method provides improved precision and robustness.