A modified calibration approach for ratio estimator in stratified sampling

Calibration is a technique for the adjustment of the original design weight to improve the precision of the survey. There is a dearth of information on calibration approach adapted for survey such that the survey cost is put into consideration. This research work developed a modified calibration approach for improving survey precision by considering the cost function. Data set on vegetable and tobacco productions (metric tonnes) were considered for this study. The data were obtained from the website of Food and Agriculture Organization. Data used was stratified based on geographical location. The population under study was divided into subpopulation of units, these subpopulations were non-overlapping homogenous sub- group. Observations were drawn within each stratum by simple random sampling with optimum allocation procedure. The proposed estimator was derived and used to determine the linear weight estimator of population parameters. The statistical properties of the derived estimator was examined. Using Lagrange multiplier, Mean Square Error and Relative Efficiency was obtained. The proposed estimator is found to be efficient