To estimate the population mean using auxiliary variable there are many estimators available in literature like-ratio, product, regression, dual-to-ratio estimator and so on. Suppose that all the information of the main variable is present in the sample but only a part of data of the auxiliary variable is available. Then, in this case none of the aforementioned estimators could be used. This paper presents an imputation based factor-type class of estimation strategy for population mean in presence of missing values of auxiliary variables. The non-sampled part of the population is used as an imputation technique in the proposed class. Some properties of estimators are discussed and numerical study is performed with efficiency comparison to the non-imputed estimator. An optimum sub-class is recommended.
Key words: Imputation, non-response, post-stratification, simple random sampling without replacement (SRSWOR), respondents (R).
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