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<p>Measurements such as rates and proportions are continuous and bounded within the interval (0, 1), and are commonly encountered in fields like healthcare, economics, social sciences, business, and physical sciences. In public health research, COVID-19 and child mortality rates are popular variables, typically bounded to this range and are longitudinal or hierarchical in nature. These characteristics require careful statistical modeling to account for their bounded and correlated nature. Thus, we proposed the New Unit-Lindley Mixed Model (NULMM) to address the modeling challenges of correlated, bounded response variables, and applied it to analyze child mortality and COVID-19 mortality rates across South Asian countries. The results indicate a positive association between COVID-19 mortality rates and both the proportion of the population aged 65 and older and time. In contrast, GDP and the number of hospital beds were found to be negatively correlated with COVID-19 mortality rates. For child mortality rates, the analysis revealed negative associations with predictors such as time, health expenditure, average years of schooling, and the Human Development Index (HDI). Finally, model comparison results consistently showed that the NULMM outperforms the existing Unit-Lindley Mixed Model (ULMM).</p>

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New unit Lindley mixed model: applications to COVID-19 and child mortality rates under the Bayesian approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月1日

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  • Nirajan Bam,
  • Laxmi Prasad Sapkota

摘要

Measurements such as rates and proportions are continuous and bounded within the interval (0, 1), and are commonly encountered in fields like healthcare, economics, social sciences, business, and physical sciences. In public health research, COVID-19 and child mortality rates are popular variables, typically bounded to this range and are longitudinal or hierarchical in nature. These characteristics require careful statistical modeling to account for their bounded and correlated nature. Thus, we proposed the New Unit-Lindley Mixed Model (NULMM) to address the modeling challenges of correlated, bounded response variables, and applied it to analyze child mortality and COVID-19 mortality rates across South Asian countries. The results indicate a positive association between COVID-19 mortality rates and both the proportion of the population aged 65 and older and time. In contrast, GDP and the number of hospital beds were found to be negatively correlated with COVID-19 mortality rates. For child mortality rates, the analysis revealed negative associations with predictors such as time, health expenditure, average years of schooling, and the Human Development Index (HDI). Finally, model comparison results consistently showed that the NULMM outperforms the existing Unit-Lindley Mixed Model (ULMM).