Abstract:
Surprising perceptions may happen in survey sampling. Te 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. Tese 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 stratifed random sampling design. Te proposed class is based on extremely enormous or
potentially small observations and robust regression tools, under the framework of Sarndal. Te class is at frst defned 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. Te
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. Tese
issues might be diminished by upgrading respondent participation by scrambled response devices/techniques that cover the
genuine value of the sensitive variable. Tus, three techniques (namely additive, mixed, and Bar-Lev) are incorporated for the
purposes of the article. Te 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
