Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

Abstract

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

Assessing the Effect of Nonresponse and Measurement Error Using a Novel Class of Efficient Estimators

“…Okafor and Lee [4] consider the ratio and regression estimator under NR in the double sampling. Some other conspicuous works on the estimation under NR are Singh and Kumar [5], Kreuter et al [6], Unal and Kadilar [7], Bii et al [8], and Pandey et al [9].…”