Estimation of mean with imputation of missing data using factor-type estimator under adaptive cluster sampling in sample surveys

Abstract

Sample surveys commonly have missing data, which calls for the use of statistical techniques to solve the issue. Evaluation of the missing data can be done using the data collected from the population and sample. This process is commonly referred to as the imputation technique. This publication introduces Adaptive Cluster Sampling (ACS) estimation method in the presence of missing observations of study variable in the sample. The Bias, M.S.E. and optimal M.S.E. of the proposed estimator are determined up to first order using the concept of large sample approximations.