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Truncated Concomitant Information for the Imputation of Missing Values

Muhammed Umair Sohail, Javid Shabbir


It is well that, utilization of additional auxiliary information in any form, can improve the performance of the estimation procedure. In present script, we proposed a class of estimators by using the supplementary and truncated auxiliary information for imputing the missing values. Mathematical results for bias and mean squared error are obtained up to first order approximation. For relative comparison of the proposed estimators with existing ones, real life data sets are used. The numerical study reveals that, the proposed class estimators can perform better than (Rao, 1991), (Grover and Kaur, 2014) and (Haq, Khan and Hussain, 2017) estimators.


Response, missing values, imputation, auxiliary truncation.

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