On the use of imputation methods for missing data in estimation of population mean under two-phase sampling design

Abstract

Non-response is an unavoidable feature in sample surveys and it needs to be carefully handled to avoid the biased estimates of population characteristics/parameters. Imputation is one of the latest fascinating methods which is attracting the attention of survey practitioners to deal with the problems of non-response because it makes the survey data complete before the beginning of generating the survey estimates. The present work proposes some new imputation methods to compensate the missing data in two-phase sampling when non-response observed in samples of both the phases. The proposed imputation methods result in chain type estimators of population mean of study variable and the resultant estimators have shown the ecacious performances in terms of producing the more precise estimates. Properties of the proposed estimators are examined with the help of empirical and simulation studies. Results are critically analyzed and suitable recommendations are put forward to the survey practitioners.

A simulation study: Robust ratio double sampling estimator of finite population mean in the presence of outliers

“…Following Kadilar et al [1], Noor-ul-Amin et al [20] applied the concept of Sukhatme [19] and provided the estimator of the mean using the Huber-M measure for double sampling. Singh et al [21] presented various imputation methods to compensate for the missing data in estimating the population mean parameter for two-phase sampling. Guha and Chandra [22] proposed an improved chain-ratio estimator for the population total based on double sampling.…”