A Modified Calibration Approach for Ratio Estimator in Stratified Sampling

Abstract

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

A Comprehensive Review on Association Rule Mining and its Algorithms: Apriori, FP Growth, and SS-FIM

“…Only then can he make the best choice for the patient. As one of the most significant data mining research techniques, the association rule is helpful in this situation [19][20][21][22][23][24][25]. In census data, association rule mining has a lot of potential.…”

New generalized class of estimators for estimation of finite population mean based on probability proportional to size sampling using two auxiliary variables: A simulation study

“…Amab 42 proposed the optimum estimation of a finite population total in PPS sampling with a replacement for multicharacter surveys. Olayiwolla et al 43 suggested the PPS method to enhance the efficiency of the estimator in two-stage sampling.…”

“…Amab 42 proposed the optimum estimation of a finite population total in PPS sampling with a replacement for multicharacter surveys. Olayiwolla et al 43 suggested the PPS method to enhance the efficiency of the estimator in two-stage sampling. In this article, the primary aim of the current work is to propose a new improved generalized class of estimators for the estimation of finite population mean using two auxiliary variables under PPS. The bias and mean squared error (MSE) of the proposed estimator is derived up to the first order of approximation. Through use of the real data sets from various domains and a simulation study, the application of the proposed estimator is highlighted. …”

“…Amab 42 proposed the optimum estimation of a finite population total in PPS sampling with a replacement for multicharacter surveys. Olayiwolla et al 43 suggested the PPS method to enhance the efficiency of the estimator in two-stage sampling. In this article, the primary aim of the current work is to propose a new improved generalized class of estimators for the estimation of finite population mean using two auxiliary variables under PPS.…”