Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Abstract

Surprising perceptions may happen in survey sampling. The arithmetic mean estimator is touchy to extremely enormous or potentially small observations, whenever selected in a sample. It can give one‐sided (biased) results and eventually, enticed to erase from the selected sample. These extremely enormous or potentially small observations, whenever known, can be held in the sample and utilized as supplementary information to expand the exactness of estimates. Also, a supplementary variable is consistently a well‐spring of progress in the exactness of estimates. A suitable conversion/transformation can be utilized for getting much more precise estimates. In the current study, regarding population mean, we proposed a robust class of separate type quantile regression estimators under stratified random sampling design. The proposed class is based on extremely enormous or potentially small observations and robust regression tools, under the framework of Särndal. The class is at first defined for the situation when the nature of the study variable is nonsensitive, implying that it bargains with subjects that do not create humiliation when respondents are straightforwardly interrogated regarding them. Further, the class is stretched out to the situation when the study variable has a sensitive nature or theme. Sensitive and stigmatizing themes are hard to explore by utilizing standard information assortment procedures since respondents are commonly hesitant to discharge data concerning their own circle. The issues of a population related to these themes (for example homeless and nonregular workers, heavy drinkers, assault and rape unfortunate casualties, and drug users) contain estimation errors ascribable to nonresponses as well as untruthful revealing. These issues might be diminished by upgrading respondent participation by scrambled response devices/techniques that cover the genuine value of the sensitive variable. Thus, three techniques (namely additive, mixed, and Bar‐Lev) are incorporated for the purposes of the article. The productivity of the proposed class is also assessed in light of real‐life dataset. Lastly, a simulation study is also done to determine the performance of estimators.