A New Class of Quantile Regression Ratio-Type Estimators for Finite Population Mean in Stratified Random Sampling

Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using...

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Bibliographic Details
Published in:Axioms 2023-07, Vol.12 (7), p.713
Main Authors: Koç, Tuba, Koç, Haydar
Format: Article
Language:English
Subjects:
Adultery
efficiency
Estimators
Mathematical research
mean square error
Mean square errors
Quantile regression
Quantiles
Random sampling
Random variables
ratio-type estimators
Regression analysis
Sample variance
Statistical sampling
stratified random sampling
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Summary:Quantile regression is one of the alternative regression techniques used when the assumptions of classical regression analysis are not met, and it estimates the values of the study variable in various quantiles of the distribution. This study proposes ratio-type estimators of a population mean using the information on quantile regression for stratified random sampling. The proposed ratio-type estimators are investigated with the help of the mean square error equations. Efficiency comparisons between the proposed estimators and classical estimators are presented in certain conditions. Under these obtained conditions, it is seen that the proposed estimators outperform the classical estimators. In addition, the theoretical results are supported by a real data application.
ISSN:2075-1680
2075-1680
DOI:10.3390/axioms12070713