Comparison of estiamators in successive sampling models

Abstract

Successive sampling is a type of sampling which is carried out on two or more occasions. Such sampling could either be on the same items or different items on the same population which is sampled repeatedly and the same study variables are measured at each occasion. Although much work has been done on successive sampling, most of the work has however been confined to using ratio and regression estimates on the matched portions of the sample on the current occasion. Less work has been done on the comparison of efficiencies of the estimators when sampling on more than two occasion as well as developing second-order autoregressive process time series estimators. The objectives of the study is to compute ratio and regression estimates in successive sampling using forest data and compare the efficiency of ratio and regression estimators. Estimators for successive sampling based on a second order autoregressive time series model will also be developed. The results show that the ratio estimators over-estimate the population mean, while the simple mean under-estimate it. The gain in precision in using the ratio estimator over the simple mean is 9.57 %, and over the regression estimators is 4.80%. Comparison of ratio and regression estimators showed that the gain in precision using regression estimator over ratio estimator is 4.78 %. A survey carried out on a single occasion cannot give better information of the natur-e of the changes or rate of such changes, because population element may change over time. A major advantage of this study is that since most populations are dynamic, regression and time series estimators would be more appropriate so. as to avoid loss of useful information.