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<p>Reliable estimation of population health indicators is essential for evidence-based public health planning and policy formulation. This study applies a ranked set sampling approach with auxiliary health information to improve the estimation of population means in large-scale health surveys. Using data from the fifth round of the National Family Health Survey (NFHS-5) for the Varanasi district of Uttar Pradesh, India, two empirical applications related to maternal and child health are examined. In the first application, maternal body mass index is used as auxiliary information to estimate infant birth weight, while in the second, maternal hemoglobin level is utilized to improve estimation of child stunting prevalence. New classes of estimators incorporating logarithmic transformations of auxiliary variables are employed and evaluated in terms of mean squared error. The empirical findings demonstrate that the proposed estimators provide consistently more precise and reliable estimates than existing methods across different sample sizes. These results highlight the practical value of ranked set sampling and auxiliary information in enhancing the quality of health estimates from national surveys, with direct implications for public health monitoring and policy evaluation.</p>

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Statistical Efficiency in Maternal and Child Health Estimation: A Rank Set Sampling Analysis of NFHS-5

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Pooja Maurya,
  • Poonam Singh

摘要

Reliable estimation of population health indicators is essential for evidence-based public health planning and policy formulation. This study applies a ranked set sampling approach with auxiliary health information to improve the estimation of population means in large-scale health surveys. Using data from the fifth round of the National Family Health Survey (NFHS-5) for the Varanasi district of Uttar Pradesh, India, two empirical applications related to maternal and child health are examined. In the first application, maternal body mass index is used as auxiliary information to estimate infant birth weight, while in the second, maternal hemoglobin level is utilized to improve estimation of child stunting prevalence. New classes of estimators incorporating logarithmic transformations of auxiliary variables are employed and evaluated in terms of mean squared error. The empirical findings demonstrate that the proposed estimators provide consistently more precise and reliable estimates than existing methods across different sample sizes. These results highlight the practical value of ranked set sampling and auxiliary information in enhancing the quality of health estimates from national surveys, with direct implications for public health monitoring and policy evaluation.