A new bivariate model is introduced by compounding negative binomial and geometric distributions. Distributional properties, including joint, marginal and conditional distributions are discussed. Expressions for the product moments, covariance and correlation coecient are obtained. Some properties such as ordering, unimodality, monotonicity and self-decomposability are studied. Parameter estimators using the method of moments and maximum likelihood are derived. Applications to trac accidents data are illustrated.
Diseases like hepatitis remained a major health concern, especially in developing countries. The awareness and knowledge about such diseases are of prime importance. The analysis of socioeconomic factors associated with the tendency of awareness and knowledge about the said diseases is fundamental. However, in developing countries like Pakistan, very few studies have considered such investigations using nationally representative data. In addition, a careful review of the literature suggests that no studies have analyzed the trends in awareness and knowledge about said disease with respect to time using nationally representative datasets. Furthermore, the existing literature regarding these studies has utilized the classical methods for the analysis. We have considered a detailed study for analyzing the trends in awareness and knowledge about the said disease in the general population of Pakistan from 2012 to 2018, using nationally representative data collected through Pakistan Demographic and Health Surveys. In addition, we have considered the Bayesian methods for the analysis and performance of the proposed Bayes methods that have been compared with the frequently used classical methods. The results indicated that the proposed Bayesian multiple logistic regression models performed better as compared to classical multiple logistic regression models (CMLRMs). This is due to fact that widths of 95% CIs were smaller for Bayesian multiple logistic regression models (BMLRM), as compared to classical multiple logistic regression models. The findings of the study suggest that there are severe disparities (with respect to different socioeconomic groups) in the knowledge and awareness of respondents for hepatitis. The levels of knowledge and awareness about the said disease are drastically low for respondents living in rural areas, having lower levels of education and wealth. These disparities seem to persist, as the corresponding odds have not changed much during the period 2012 to 2018. The policy-maker should plan and implement the strategies to reduce the observed disparities for different sectors of society.
The literature contains a number of studies to analyze the important factors relating to maternal and child health care (MCH). However, the earlier contributions have employed classical models for the analysis. We have proposed Bayesian models for exploring the factors regarding MCH in Pakistan. The latest data, from Pakistan Demographic and Heath Survey (PDHS) conducted in 2017-18, have been used for analysis. The performance of Bayesian methods have been compared with classical methods based on various goodness-of-fit criteria. The performance of Bayesian methods was observed to be better than the classical methods. The results advocated that 86.20% of mothers received antenatal care (ANC), while only 51.40% of the mothers received it at least for ANC visits during the whole pregnancy period. Further, 68.90% of the mothers were protected against neonatal tetanus. More than 30% of women neither delivered in the health facility place nor they were in receipt of postnatal checkups. Additionally, only three out of five newborns were availed with postnatal checkup (PNC) within two days of their births. About 66.89% of women reported problems in accessing the MCH in the country. The study also suggested the presence of severe disparities among different socio-economic groups in availing MCH. There is immediate need to reduce these disparities among various socio-economic groups in the country.
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