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Estimation of general parameters for sensitive study variables using auxiliary information for finite population

Author(s)
Amjad Mahmood Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan.; Hailey College of Commerce, University of the Punjab, Lahore, Pakistan.
Nadia Mushtaq Department of Statistics, Forman Christian College (A Chartered University),Lahore,Pakistan
Muhammad Hanif Department of Statistics, National College of Business Administration and Economics,Lahore,Pakistan
Farhad Hussain Department of Management Science and Engineering, Hebei University,Baoding, Hebei,China
Abstract
The judgment of parameters about the populace is significant for drawing a sample from the population under study in the survey method. Innumerable statisticians introduced numerous estimators to make predictions about the parameters in a population with the application of auxiliary information for sensitive variables. In the current investigation, the researchers tried to depict the general parameter estimate for sensitive variables using randomized response models. The survey was the method in this paper, and a simple random without replacement (SRSWOR) was utilized to gather the sample. Overall, it presented the general ratio and exponential ratio of estimations for the sensitive variable using non-sensitive AV founded on an RRT. The biasness and MSE expressions above second category calculations appeared as outcomes. Many empirical works are replicated to prove the performance of projected estimators for the sensitive variables for the population under study. This proven model will benefit other researchers and statisticians working in the statistics field or data collection, for instance, population census, to take forward it and develop more advanced statistical general parameters, and also for advanced investigations.
Publication Details
Page(s) 17-36
DOI 10.47264/idea.nasij/4.1.2
Published Journal: Natural and Applied Sciences International Journal (NASIJ), Volume: 4, Issue: 1, Year: 2023

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Keywords
Auxiliary information, parameter estimators, sensitive variables, inconsistent, randomized response model, simulation studies
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