Construction of an Almost Unbiased Estimator for Population Variance Using Exponential - Sine Type Estimator
DOI:
https://doi.org/10.62933/mz0yvj11Keywords:
Population variance, Auxiliary information, Exponential - Sine estimator, Mean square error, Regression estimatorAbstract
In this paper, we have proposed a generalized almost unbiased estimator for estimating the unknown finite population variance of the study variable , using auxiliary information under the exponential-cum-sine estimator framework. In many applications, the presence of bias in an estimator can be a significant drawback. Following the procedure of Singh and Singh (1991, 1993), we develop an estimator based on a single auxiliary variable, which is almost unbiased up to the order . Expressions for the bias and mean squared error (MSE) of the proposed estimator are derived up to the first order of approximation. To support the theoretical results, an empirical study using two real-life data sets has been conducted. Additionally, a simulation study confirms that the proposed estimator has lower bias (nearly zero) and achieves a minimum MSE equivalent to that of the regression estimator.
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