Department of Statistics
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Item Mediation effects of individuals’ sexual quality of life on the relationship between sexual quality of life of partners and its predictors: a path analysis(Journal of Public Health and Development, 2022) Alidu, H.; Nortey, E.N.N.; Adedia, D.; et al.Sexual function and the sexual quality of life of an individual could be influenced by several factors. Sexual dysfunction could affect the sexual quality of life of the individual and, possibly have an impact on their partners. Treatments that improve sexual function among individuals tend to improve the sexual quality of life of their partners. This study explored the mechanism by which an individual’s sexual quality of life mediates the relationship between factors, such as age, sexual dysfunction, perception of IELT, and the partner’s sexual quality of life. Path analysis was used to determine if this effect was via direct or indirect mechanisms. Outpatients attending the Maamobi and Tema General Hospitals, as well as their partners, recruited for this study. A total of 130 males and their partners, as well as 116 females and their partners, were recruited. The GRISS was used to evaluate the sexual function of participants. The sexual quality of life questionnaire was used to evaluate participants and their partners. Ageing in both sexes had a direct effect on the sexual quality of life of their partners. Ageing also indirectly compromises the sexual quality of life of male partners. Impotence indirectly affects the sexual quality of life of female partners. Vaginismus indirectly affected the sexual quality of life of their male partners. Among the male participants, avoidance of sexual activity had both direct and indirect effects on the sexual quality of life of their female partners. avoidance of sexual activity by female participants only had an indirect effect on the sexual quality of life of the male partners. Aging in both sexes directly compromises the sexual quality of life of their partners.Item Time series based road traffic accidents forecasting via SARIMA and Facebook Prophet model with potential changepoints(Heliyon, 2023) Agyemang, E.F.; Mensah, J.A.; Ocran, E.; Opoku, E.; Nortey, E.N.N.Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana’s Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning from 2013 to 2018. Their predictive accuracies were then evaluated using the test set, comprising monthly RTA data from 2019. The study employed the Box-Jenkins method on the training set, yielding the development of various tentative time series models to effectively capture the patterns in the monthly RTA data. 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 was found to be the suitable model for forecasting RTAs with a log-likelihood value of −266.28, AIC value of 538.56, AICc value of 538.92, BIC value of 545.35. The findings disclosed that the 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 model developed outperforms FB-Prophet with a forecast accuracy of 93.1025% as clearly depicted by the model’s MAPE of 6.8975% and a Theil U1 statistic of 0.0376 compared to the FB-Prophet model’s respective forecasted accuracy and Theil U1 statistic of 84.3569% and 0.1071. A Ljung Box test on the residuals of the estimated 𝑆𝐴𝑅𝐼𝑀𝐴 (0, 1, 1) × (1, 0, 0)12 model revealed that they are independent and free from auto/serial correlation. A Box-Pierce test for larger lags also revealed that the proposed model is adequate for forecasting. Due to the high forecast accuracy of the proposed SARIMA model, the study recommends the use of the proposed SARIMA model in the analysis of road traffic accidents in GhanaItem Baseline comparative analysis and review of election forensics: Application to Ghana’s 2012 and 2020 presidential elections(Heliyon, 2023) Agyemang, E.F.; Nortey, E.N.N.; Minkah, R.; Asah-Asante, K.Many allegations have been levelled against the electoral process of many countries across the world by most opposition leaders, especially when they lose a presidential election e.g. Ghana in 2012 and 2020. Therefore, the need to apply election forensic techniques to the certified election results data of valid votes count to statistically verify if some suspected or possible anomalies and irregularities exist in the voting pattern. This paper seeks to provide a comprehensive review of election forensics techniques and make a comparative analysis of Benford’s Second-order test of conformity (using the first two digits) and Hartigans’ dip test of unimodality to examine the existence of possible anomalies and irregularities in the 2012 and 2020 presidential elections held in Ghana. The findings of the two tests suggest that the electoral process produced possible anomalous data in the 2012 presidential election results (with an overall 16.67% suspected anomalies), whilst possible non-anomalous data was produced in the 2020 presidential election results (with an overall 0% suspected anomaly) of valid votes count. Therefore, the study recommends that for better statistical data analysis on election anomaly detection, Benford’s test of conformity and Hartigans’ dip test of unimodality should serve as baseline tests (initial screening tools), highlighting areas that may require further investigation or more rigorous analysis and progressively dig deeper into the application of finite mixture fraud models and machine learning techniques. In spite of the promising results Benford’s Law, dip test, machine learning algorithms, and network analysis have produced in detecting irregularities in election data, real-world applications remain challenging, particularly when dealing with complex and evolving forms of fraud. Therefore, there is the need for continuous research and innovation to improve the accuracy and effectiveness of these methods and promote transparency and accountability in democratic societiesItem Modeling variations in the cedi/dollar exchange rate in Ghana: an autoregressive conditional heteroscedastic (ARCH) models(Springer Plus, 2015) Quaicoe, M.T.; Twenefour, F.B.K.; Baah, E.M.; Nortey, E.N.N.This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non station ary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box–Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.Item Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression(Hindawi, 2021) Nortey, E.N.N.; Pometsey, R.; Asiedu, L.; Iddi, S.; Mettle, F.O.Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. +e model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. +e overall accuracy of the fitted model was assessed to be 92%. +rough the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.Item Recognition of Face Images under Angular Constraints Using DWT-PCA/SVD Algorithm(Far East Journal of Mathematical Sciences, 2017-12) Asiedu, L.; Mettle, F.O.; Nortey, E.N.N.; Yeboah, E.S.The intricacy of a face’s features originates from continuous changes in the facial features that take place over time. Regardless of these changes, we are able to recognize a person very easily. In human interactions, the articulation and perception of constraints; like head-poses, facial expressions form a communication channel that is additional to voice and that carries crucial information about mental, emotional and even physical states of a conversation. Automatic face recognition is worthwhile, since an efficient and resilient recognition system is useful in many application areas. This paper presents an evaluation of the performance of principal component analysis with singular value decomposition using discrete wavelet transform (DWT-PCA/SVD) for preprocessing under angular constraints. Ten individuals from Massachusetts Institute of Technology (MIT) database (2003-2005) captured under the specified angular constraints were considered for recognition runs. Friedman’s rank sum test was used to ascertain whether significant differences exist between the median recognition distances of the various constraints from their straight-pose. Recognition rate and runtime were adopted as the numerical evaluation methods to assess the performance of the study algorithm. All numerical and statistical computations were done using Matlab. The results of the Friedman’s rank sum test show that the higher the degrees of head-pose, the larger the recognition distances and that at and above, the recognition distances become profoundly larger compared to the head-pose. The numerical evaluations show that DWT-PCA/SVD face recognition algorithm has an appreciable average recognition rate (87.5%) when used to recognize face images under angular constraints. Also, the recognition rate decreases for head-poses greater than Discrete wavelet transform is recommended as a viable noise removal mechanism that should be adopted during image preprocessing.Item Modelling Vehicular Crash Mortalities in Ghana(Model Assisted Statistics and Applications, 2018) Somua-Wiafe, E.; Asare-Kumi, A.; Nortey, E.N.N.; Iddi, S.Deaths due to road accidents are major concern to many stakeholders in Ghana especially because road accidents only come second behind malaria for cause of deaths. Statistical models can be helpful in evaluating the effect of factors responsible for mortality and morbidity during vehicular accidents. There is often a spoilt for choice on the type of models that may be used to explain a particular phenomenon. Picking a model can be based on the researcher’s knowledge or experience and the simplicity of the model. However, in common applications, the models applied are often not adequate to accurately and efficiently explain underlying phenomenon particularly when it fails to address certain characteristics of the data. In this paper, an appropriate statistical model on the number of vehicular deaths in Ghana is fitted. The Poisson, Negative Binomial (NB), Zero-Inflation Poisson (ZIP) and Zero-Inflation Negative Binomial (ZINB) models, estimated by the method of maximum likelihood, are compared to determine the most appropriate model for the data at hand. In addition, due to the large number of explanatory variables, the backward model selection procedure was adopted to select the most significant factors associated with crash fatalities. After a careful model building process, the ZINB model was identified as the most appropriate for modelling road crash mortality. The model also identified factors such as shoulder type, time of crash, driver’s sex, road environment landmarks, among others as having significant effect on the fatalities during vehicular accidents in Ghana. It is recommended that authorities focus on installing reflective markings on the shoulders of roads and increase education of drivers in adhering to road regulations while also paying keen attention to road environmental landmarks.Item Estimation of the Gini coefficient for the lognormal distribution of income using the Lorenz curve(SpringerPlus, 2016-07) Darkwah, K.A.; Nortey, E.N.N.; Lotsi, A.The main objective of the study is to compare the Newton–Cotes methods such as the Trapezium rule, Simpson 1/3 rule and Simpson 3/8 rule to estimate the area under the Lorenz curve and Gini coefficient of income using polynomial function with degree 5. Comparing the Gini coefficients of income computed from the Polynomial function with degree 5 for the Trapezium, Simpson 1/3 and Simpson 3/8 methods using the relative errors showed that the trapezium rule, Simpson’s 1/3 rule and Simpson’s 3/8 rule show negative biases with the Simpson 1/3 rule yielding the lowest absolute relative true error of 4.230711 %. © 2016, The Author(s).Item Modeling inflation rates and exchange rates in Ghana: application of multivariate GARCH models(2015-02) Nortey, E.N.N.; Ngoh, D.D.; Doku-Amponsah, K.; Ofori-Boateng, K.This paper was aimed at investigating the volatility and conditional relationship among inflation rates, exchange rates and interest rates as well as to construct a model using multivariate GARCH DCC and BEKK models using Ghana data from January 1990 to December 2013. The study revealed that the cumulative depreciation of the cedi to the US dollar from 1990 to 2013 is 7,010.2% and the yearly weighted depreciation of the cedi to the US dollar for the period is 20.4%. There was evidence that, the fact that inflation rate was stable, does not mean that exchange rates and interest rates are expected to be stable. Rather, when the cedi performs well on the forex, inflation rates and interest rates react positively and become stable in the long run. The BEKK model is robust to modelling and forecasting volatility of inflation rates, exchange rates and interest rates. The DCC model is robust to model the conditional and unconditional correlation among inflation rates, exchange rates and interest rates. The BEKK model, which forecasted high exchange rate volatility for the year 2014, is very robust for modelling the exchange rates in Ghana. The mean equation of the DCC model is also robust to forecast inflation rates in Ghana. © 2015, Nortey et al.; licensee Springer.Item Extreme value modelling of Ghana stock exchange index(Springerplus, 2015-11) Nortey, E.N.N.; Asare, K.; Mettle, F.O.Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000–2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q–Q, P–P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD model.