Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

Abstract

Assessment of coefficient of variation (CV) is of major importance in numerous examinations. However, the appearance of extreme observations raises concerns about the outcomes of CV estimates based on conventional moments. So, motivated by some recent developments in finite sampling theory, we propose some new estimators of CV based on the properties of linear moments (L-moments and Trimmed L-moments), which are highly robust whenever outliers or extreme observations appear in a dataset.The proposed estimators are initially established on the premise that the variable of interest is nonsensitive which deals with the subjects that do not embarrass respondents when asked about them explicitly. These estimators are also applied to situations where the variable of interest is associated with sensitive issues that cause measurement errors resulting from nonresponse or unreliable reporting where such issues can be mitigated by increasing respondent participation by scrambled response models which obscure the true value of the sensitive variable. Four models are considered for this article: additive, multiplicative, mixed, and combined additive-multiplicative models. Finally, in both nonsensitive and sensitive settings, real-life data sets are employed to undertake simulation-based analysis. In all cases, the proposed estimators considerably increased efficiency.

Some Improved Classes of Estimators in Stratified Sampling Using Bivariate Auxiliary Information

“…Shahzad et al [2] introduced a novel family of variance estimators based on L-moments and calibration approach under strati ed simple random sampling (SSRS), whereas Shahzad et al [3] suggested L-Moments and calibration-based variance estimators under double SSRS and discussed an application of COVID-19 pandemic. Shahzad et al [4] considered the estimation of coe cient of variation using L-moments and calibration approach for nonsensitive and sensitive variables, whereas Shahzad et al [5] developed variance estimation based on L-moments and auxiliary information. e estimation of population mean is a widely discussed approach in sample surveys and many renowned authors have utilized these auxiliary pieces of information at estimation stage and suggested various modi ed estimators to date.…”

Robust ratio‐type estimators for finite population mean in simple random sampling: A simulation study

“…Ratio estimators are often used to estimate the population mean when the correlation between study and auxiliary variables is positively high. Such as Irfan et al, 1 Shahzad et al, 2 and Shahzad et al 3 introduced a class of estimators utilizing supplementary information under simple random sampling scheme when study variable is nonsensitive or sensitive. Shahzad et al 4 proposed a ratio estimator for population variance using L‐Moments and calibration technique.…”