Generalized Direct Product Type Estimators for Mean of Domain Using Supplementary Variable in Missing Data
Keywords:
Mean of domain, imputation method, supplementary variable, bias, mean squared errorAbstract
Several research works for estimating the mean of population in case of missing data have been done but a very few research works for estimating the mean of domain in case of missing data has been found. Domain means estimation in missing data is also very essential in real life. Bhushan et al. (2024a) first have proposed the estimators for mean of domain using imputation techniques in case of missing data. The main aim of the present research paper is to extend the research works of Bhushan et al. (2024a) by proposing the different imputation techniques. So, keeping this point in the view, we have suggested generalized direct product type estimators for estimating the mean of domain using information on supplementary variable in missing data. The estimators bias and mean square error are obtained. Some of the earlier existing estimators are found as the special cases of the proposed estimators and efficiency comparisons of the proposed estimators are carried with the other earlier existing estimators. Simulation studies are conducted to check the usefulness of the estimators.
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