It is well known that ranked set sampling (RSS) is more efficient than simple random
sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results …

Missing data is an obligatory phenomenon in most of the sample surveys and when it is not
handled cautiously at beginning of the study, outcomes may result to substantial biases in …

In this paper, we intend to utilize the multi auxiliary information available under RSS for the
imputation of missing data. The mean imputation, regression imputation methods, and …

Ranked set sampling (RSS) is known to increase the efficiency of the estimators while
comparing it with simple random sampling. The problem of missingness creates a gap in the …

In real life, situations may arise when the available data are insufficient to provide accurate
estimates for the domain, the small area estimation (SAE) technique has been used to get …

This paper addresses some classes of combined and separate imputation methods (CSIMs)
of the population mean under stratified simple random sampling (SSRS) along with their …

The data we encounter in real life often contain missing values. In sampling methods,
missing value imputation is done with different methods. This article proposes novel …

Some e cient product type exponential imputation methods are proposed in this article to
tackle the problem of incomplete values in sampling theory. To investigate the effectiveness …

Several imputation schemes and estimators have been proposed by different authors in
sample survey. However, these estimators utilized quantitative information of auxiliary …

Several imputation techniques have been constructed to sort out the missing data issue.
However, there are few papers that address the missing data issue in measurement error …