Comparative Analysis of Ratio-Type Exponential 
Estimators Using Monte Carlo Simulation

Sananda Kumar Jhankar; Nirupama Sahoo

doi:https://doi.org/10.14445/22315373/IJMTT-V72I3P102

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 72 | Issue 3 | Year 2026 | Article Id. IJMTT-V72I3P102 | DOI : https://doi.org/10.14445/22315373/IJMTT-V72I3P102

Comparative Analysis of Ratio-Type Exponential Estimators Using Monte Carlo Simulation

Sananda Kumar Jhankar, Nirupama Sahoo

Received	Revised	Accepted	Published
15 Jan 2026	19 Feb 2026	10 Mar 2026	26 Mar 2026

Citation :

Sananda Kumar Jhankar, Nirupama Sahoo, "Comparative Analysis of Ratio-Type Exponential Estimators Using Monte Carlo Simulation," International Journal of Mathematics Trends and Technology (IJMTT), vol. 72, no. 3, pp. 6-12, 2026. Crossref, https://doi.org/10.14445/22315373/IJMTT-V72I3P102

Abstract

This paper describes an exponential estimator of ratio-type for the finite population mean that utilizes a single auxiliary variable. Unlike existing estimators, the proposed estimator approach aims to provide a more precise estimate of the population mean. The bias and mean square error of the proposed estimator are derived up to the first order of approximation. A Monte Carlo simulation study has been conducted to evaluate the empirical performance of the proposed estimator. Four types of bivariate population distributions (Normal, Exponential, Uniform, and Gamma)were generated to represent different real-world scenarios with varying degrees of skewness and kurtosis. The Simulation compared the proposed estimator with existing ratio-type and exponential estimators in terms of Mean Squared Error (MSE) and Percent Relative Efficiency (PRE). The simulation results consistently demonstrated that the proposed estimator performs better than the existing estimators. Specifically, it yielded lower MSE values and higher PRE values, indicating greater accuracy and efficiency.

Keywords

Ratio Estimator, Bias, Mean Squared Error, Percent Relative Efficiency, Simulation.

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