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An Improved Estimation Procedure for Population Mean in the Presence of Non-Response
Un procedimiento de estimación mejorado para la media poblacional en presencia de no respuesta
DOI:
https://doi.org/10.15446/rce.v49n1.120337Keywords:
Non–response, Bias, Mean square error, Simulation study (en)Error cuadrático medio, Estudio empírico, Falta de respuesta, Sesgo (es)
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This article proposes improved estimation procedures for the population mean in the presence of non-response using auxiliary information. Based on the Hansen–Hurwitz subsampling approach, two generalized exponential-type estimators are introduced for situations where non-response occurs either only on the study variable or on both the study and auxiliary variables. The proposed estimators incorporate tuning constants and an optimization parameter to minimize the mean square error (MSE) and generate optimum versions within each class. Expressions for the bias and MSE of the estimators are derived to the first order of approximation, and the efficiency comparisons of the proposed estimators with the existing estimators are established. A comprehensive empirical evaluation demonstrates that the proposed classes consistently provide more precise estimates than the traditional estimators. The results confirm that the proposed methodology provides an efficient alternative for mean estimation under non-response settings.
Este artículo propone procedimientos de estimación mejorados para la media poblacional en presencia de no respuesta, utilizando información auxiliar. Basándose en el enfoque de submuestreo de Hansen–Hurwitz, se introducen dos estimadores generalizados de tipo exponencial para situaciones en las que la no respuesta ocurre solo en la variable de estudio o en ambas, la variable de estudio y las variables auxiliares. Los estimadores propuestos incorporan constantes de ajuste y un parámetro de optimización para minimizar el error cuadrático medio (ECM) y generar versiones óptimas dentro de cada clase. Se derivan expresiones para el sesgo y el ECM de los estimadores hasta el primer orden de aproximación, y se establecen comparaciones de eficiencia entre los estimadores propuestos y los estimadores existentes. Una evaluación numérica exhaustiva demuestra que las clases propuestas proporcionan consistentemente estimaciones más precisas que los estimadores tradicionales. Los resultados confirman que la metodología propuesta ofrece una alternativa eficiente para la estimación de la media en contextos de no respuesta.
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