Gabriel Kallah-Dagadu

The Ghana Living Standard Survey reports show increased consumption of water packaged in polyethylene film known as sachet water. These plastic films used in packaging the water contain compounds such as Bisphenol A and Phthalate. These are added during the manufacturing process to enhance certain characteristics of the plastics but can migrate from the water packaging material into the contained water. In this regard, these migrating compounds have raised many concerns due to their suspected endocrine disruption effects in humans. The study was to determine Di-Methyl phthalate, Di-Ethyl phthalate, Di-Butyl phthalate, Benzyl n-butyl phthalate, Di (2-ethyl hexyl) phthalate and Bisphenol A and identify their migration trends. Five different brands of sachet water were randomly selected and stored at temperatures of 5˚C, 27˚C, and 50˚C for 28 days and their migration trends were determined using Gas Chromatography coupled with Mass Spectrometry (GC-MS) after the extraction process. The levels of phthalates in sachet water increased with increasing temperatures and storage period with Di (2-ethyl hexyl) phthalate being highest (165.34 ppb) exceeding the World Health Organization guideline of 8 ppb (µg/L). Their levels in water could be embryotoxic and teratogenic to humans via consumption. Bisphenol A was also detected at levels within the range of 0.61 - 57.22 ppb, which was non toxic. Therefore, there should be a legislation to regulate the type and amount of phthalate and Bisphenol A used in sachet polyethylene film. Regulatory bodies should thus, must educate sachet water merchants on appropriate storage temperatures and period.

Classification is one of the main areas of machine learning, where the target variable is usually categorical with at least two levels. This study focuses on deducing an optimal cut-off point for continuous outcomes (e.g., predicted probabilities) resulting from binary classifiers. To achieve this aim, the study modified univariate discriminant functions by incorporating the error cost of misclassification penalties involved. By doing so, we can systematically shift the cut-off point within its measurement range till the optimal point is obtained. Extensive simulation studies were conducted to investigate the performance of the proposed method in comparison with existing classification methods under the binary logistic and Bayesian quantile regression frameworks. The simulation results indicate that logistic regression models incorporating the proposed method outperform the existing ordinary logistic regression and Bayesian regression models. We illustrate the proposed method with a practical dataset from the finance industry that assesses default status in home equity.

Background. Annually, 5.4 million children under five face mortality, with 2.5 million deaths in the first month, 1.6 million between one and eleven months, and 1.3 million aged one to four. Despite global strides, sub-Saharan Africa, including Ghana, grapples with persistent high child mortality. This study employs statistical methods to pinpoint factors driving under-five mortality in the Greater Accra Regional Hospital. Methods. The data was acquired from Greater Accra Regional Hospital, Ghana, spanning January to December 2020. The data comprised all under-five deaths recorded in the hospital in 2020. The statistical tools employed were the chi-square test of association and the multinomial logistic regression model. Results. In 2020, there were 238 cases of under-five mortality recorded in the hospital, with males constituting the majority (55). About 85 of these cases occurred within the first month of birth, primarily attributed to respiratory distress, prematurity, and sepsis. Notably, meconium aspiration was the least common among grouped diagnoses. The test of association and multinomial logistic model emphasised the childs age, birth type, and weight at birth as significant factors influencing child mortality. Conversely, attributes like sex, marital status, and mothers age displayed no notable association with the diagnosis of death. Conclusion. The study on child mortality at the Greater Accra Regional Hospital unveils key factors shaping child health outcomes, emphasising the role of age, birth type, and weight. While specific demographics show no significant association, identified predictors are vital for targeted interventions. Proposed strategies encompass education programs, improved care, birthing practices, and data-driven policies.

In this paper, we apply Markov chain techniques to select the best financial stocks listed on the Ghana Stock Exchange based on the mean recurrent times and steady-state distribution for investment and portfolio construction. Weekly stock prices from Ghana Stock Exchange spanning January 2017 to December 2020 was used for the study. A three-state Markov chain was used to estimate the transition matrix, long-run probabilities, and mean recurrent times for stock price movements from one state to another. Generally, the results revealed that the long-run distribution of the stock prices showed that the constant state recorded the highest probabilities as compared to the point loss and point gain states. However, the results showed that the mean recurrent time to the point gain state ranges from three weeks to thirty-five weeks approximately. Finally, Standard Chartered Bank, GCB, Ecobank, and Cal Bank emerged as the top best performing stocks with respect to the mean recurrent times and steady-state distribution, and therefore, these equities should be considered when constructing asset portfolios for higher returns.

The main objective of this study is to propose a method of analysing the volatility of a seemingly random walk time series with correlated errors without transforming the series as performed traditionally. The proposed method involves the computation of moving volatilities based on sliding and cumulative data matrices. Our method rests on the assumption that the number of subperiods for which the series is available is the same for all periods and on the assumption that the series observations in each subperiod for all the periods under consideration are a random sample from a particular distribution. The method was successfully implemented on a simulated dataset. A paired sample -test, Wilcoxon signed rank test, repeated measures (ANOVA), and Friedman tests were used to compare the volatilities of the traditional method and the proposed method under both sliding and cumulative data matrices. It was found that the differences among the average volatilities of the traditional method and sliding and cumulative matrix methods were insignificant for the simulated series that follow the random walk theorem. The implementation of the method on exchange rates for Canada, China, South Africa, and Switzerland resulted in adjudging South Africa to have the highest fluctuating exchange rates and hence the most unstable economy.

In this paper, we introduce reduced‐bias estimators for the estimation of the tail index of Pareto‐type distributions. This is achieved through the use of a regularised weighted least squares with an exponential regression model for log‐spacings of top‐order statistics. The asymptotic properties of the proposed estimators are investigated analytically and found to be asymptotically unbiased, asymptotically consistent, and asymptotically normally distributed. Also, the finite sample behaviour of the estimators are studied through a simulation study The proposed estimators were found to yield low bias and mean square errors. In addition, the proposed estimators are illustrated through the estimation of the tail index of the underlying distribution of claims from the insurance industry.