Abstract:
This study carries on the problem of nonparametric estimation of nite population total
using multiplicative bias correction technique for two sample problem in a sample survey is
considered. Let two separate surveys collect related information on a single population U.
Consider a situation where we want to best combine data from the two surveys to yield a
single set of estimates of a population quantity (\population parameter") of interest. This
thesis presents a multiplicative bias reduction estimator for nonparametric regression to two
sample problem in a sample survey. The approach consists of applying a multiplicative bias
correction to an estimator. The multiplicative bias correction method which was proposed,
by Linton & Nielsen, 1994, assures a positive estimate and reduces the bias of the estimate
with negligible increase in variance. Even as we apply this method to the two sample
problem in a sample survey, we found out through the study of it asymptotic properties that
it was asymptotically unbiased and statistically consistent. Furthermore, an empirical study
was carried out to compare the performance of the developed estimator with the existing
ones. The theoretical and empirical results led to the conclusion that the multiplicative bias
corrected estimator can be highly recommended for two sample problem in sample survey
when estimating the nite population total.
