Modeling the Prevalence of Anemia among the Children in Pakistan: Classical and Bayesian Estimation Approach

Authors

  • Tahira Bano Qasim Department of Statistics, The Women University, Multan, Pakistan
  • Dua Israr Department of Statistics, The Women University, Multan, Pakistan
  • Maria Ghaffar Department of Statistics, The Women University, Multan, Pakistan

DOI:

https://doi.org/10.52700/scir.v5i2.134

Keywords:

Bayesian Estimation, Lindley Approximation, Anemia, Pakistani Children.

Abstract

Anemia remains a major public health concern in all nations, particularly affecting children worldwide. This disorder, which is defined as low hemoglobin or red blood cells in the blood, has multiple causes, the most common one being iron deficiency. The prevalence of anemia among children in Pakistan have been modeled by conducting a thorough statistical analysis emphasizing on inferential statistics and estimation techniques. The study explores the classical and non-classical essential categories of estimating methodologies. The work specifically looks into Bayesian estimation methods under squared error loss function (SELF) and weighted square error loss function (WSELF) for a new generalization of the Exponential Distribution introduced by Nadarajah Haghighi(2017) named as Nadarajah Haghighi Distribution (NHD). The Bayesian estimation of the parameters of NH distribution is run under uniform prior (Non-Informative) and exponential prior (Informative prior). The Lindley approximation method is utilized in this study to solve Bayesian integral. This methodology's practical application focuses on determining Pakistan's under-five-year-old population's anemia prevalence. Both maximum likelihood and Bayesian estimators by means of Monte Carlo simulations and practical application are used to thoroughly compared. Notably, it is empirically reveal that Bayesian estimation technique performs better than the classical estimation technique both in case of simulation and in modeling the prevalence of anemia among Pakistani children.

Published

2023-12-31