The proposed estimators outperform existing ones with lower mean squared error and higher efficiency.
Authors
Sohaib Ahmad, Department of Statistics, Quaid-I-Azam University, Islamabad, Pakistan
Kalim Ullah, Department of Anesthesiology, Aga Khan University, Karachi, Pakistan
Javid Shabbir, Department of Statistics, University of Wah, Wah Cantt, Pakistan
Awadhesh K. Pandey, Assistant Professor, Jindal School of Banking and Finance, O.P. Jindal Global University, Sonipat, Haryana, India
Summary
In face of diverse environmental challenges, such as geographic, economic, and physical factors, require an understanding key demographic dynamics is essential for evaluating data and making predictions. Indicators like soil quality, rainfall, water quality, and air quality demonstrate how statistical analysis can be used to better understand and define environmental conditions. Multiple studies have been done to examine the difficulties of predicting the populations mean, variance, median, and total using auxiliary information. The aim of this article is to develop an enhanced estimator for analyzing the finite population distribution function (DF) under simple random sampling. The bias and mean squared error (MSE) are derived up to the first degree of approximation. The novelty and robustness of our suggested estimators is evaluated through a simulation study and three real data sets. The suggested estimators are better in terms of minimum MSE, as shown by their significant improvements in efficiency. Our suggested estimators have a significantly lower MSE, making them far more efficient than the contemporary estimators. The numerical results show that compared to all the existing estimators evaluated in this study, the suggested estimators are the most effective.
Published in: SN Computer Science
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