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New Calibration Estimators of Population Variance in the Presence of Random Non-Response under Successive Sampling Scheme
Nuevos estimadores de calibración de la varianza poblacional en presencia de falta de respuesta aleatoria bajo un esquema de muestreo sucesivo
DOI:
https://doi.org/10.15446/rce.v49n1.119505Keywords:
Calibration estimator, Mean square error, Random non-response, Two-occasion sampling (en)Error cuadrático medio, Estimador de calibración, Muestreo en dos ocasiones, Respuesta no aleatoria (es)
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This research addresses the challenge of estimating population variance in surveys conducted over two-occasion (successive) sampling, particularly when dealing with non-response. The study introduces a traditional estimator and two new calibration-based estimators to mitigate the impact of non-response. These calibration estimators are designed to improve the accuracy and reliability of estimates derived from successive sampling surveys, where non-sampling errors can significantly distort the data and the resulting population parameters. The study provides expressions for the proposed estimators and analyzes their statistical properties. Simulation studies reveal that the calibration estimators outperform the traditional estimator in terms of bias, mean squared error, and relative absolute bias, especially when non-response rates are high.
Esta investigación aborda el reto de estimar la varianza poblacional en encuestas realizadas con muestreos sucesivos, en particular en el caso de la falta de respuesta. El estudio introduce un estimador tradicional y dos nuevos estimadores basados en calibración para mitigar el impacto de la falta de respuesta. Estos estimadores de calibración están diseñados para mejorar la precisión y la fiabilidad de las estimaciones derivadas de encuestas con muestreos sucesivos, donde los errores no muestrales pueden distorsionar significativamente los datos y los parámetros poblacionales resultantes. El estudio proporciona expresiones para los estimadores propuestos y analiza sus propiedades estadísticas. Estudios de simulación revelan que los estimadores de calibración superan al estimador tradicional en términos de sesgo, error cuadrático medio y sesgo absoluto relativo, especialmente cuando las tasas de falta de respuesta son altas.
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