Imputation based mean estimators in case of missing data utilizing robust regression and variance-covariance matrices

Missing data is a common problem in sample surveys and statisticians have recognized that statistical inference can be spoiled in the presence of non-response. Kadilar and Cingi built up a class of estimators for assessing the population mean under simple random sampling scheme when there are missing observations in the data set. This article firstly, proposes a class of estimators in light of Zaman and Bulut work, and after that defines another class of regression type estimators utilizing robust regression tools, robust variance-covariance matrices and supplementary information. The use of robust techniques in Zaman and Bulut ratio type estimators enable us to estimate the population mean in several cases of missing observations. The hypothetical mean square error equations are also derived for adapted and proposed estimators. These hypothetical discoveries are assessed by the numerical illustration, in support of present work.

Automated summary

This article proposes a class of estimators based on robust regression techniques for assessing the population mean in the presence of missing observations in a data set under a simple random sampling scheme. The effectiveness of the proposed method is validated through numerical illustrations.