Browsing by Author "Frempong, Nana Kena"
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- ItemA Mixture of Clayton, Gumbel, and Frank Copulas: A Complete Dependence Model(Hindawi, 2022) Boateng , M. A.; Omari-Sasu, A. Y.; Avuglah, R. K.; Frempong, Nana Kena; 0000-0002-7138-3526Knowledge of the dependence between random variables is necessary in the area of risk assessment and evaluation. Some of the existing Archimedean copulas, namely the Clayton and the Gumbel copulas, allow for higher correlations on the extreme left and right, respectively. In this study, we use the idea of convex combinations to build a hybrid Clayton–Gumbel–Frank copula that provides all dependence scenarios from existing Archimedean copulas. (e corresponding density and conditional distribution functions of the derived models for two random variables, as well as an estimator for the proportion parameter associated with the proposed model, are also derived. (e results show that the proposed model is able to show any case of dependence by providing coefficients for the upper tail and lower tail dependence.
- ItemA New Generated Family of Distributions: Statistical Properties and Applications with Real-Life Data(Wiley / Hindawi, 2023) Okutu, John Kwadey; Frempong, Nana Kena; Appiah, Simon K.; Adebanji, Atinuke O.; 0000-0002-7138-3526Several standard distributions can be used to model lifetime data. Nevertheless, a number of these datasets from diverse fields such as engineering, finance, the environment, biological sciences, and others may not fit the standard distributions. As a result, there is a need to develop new distributions that incorporate a high degree of skewness and kurtosis while improving the degree of goodness-of-fit in empirical distributions. In this study, by applying the T-X method, we proposed a new flexible generated family, the Ramos-Louzada Generator (RL-G) with some relevant statistical properties such as quantile function, raw moments, incomplete moments, measures of inequality, entropy, mean and median deviations, and the reliability parameter. The RL-G family has the ability to model “right,” “left,” and “symmetric” data as well as different shapes of the hazard function. The maximum likelihood estimation (MLE) method has been used to estimate the parameters of the RL-G. The asymptotic performance of the MLE is assessed by simulation analysis. Finally, the flexibility of the RL-G family is demonstrated through the application of three real complete datasets from rainfall, breaking stress of carbon fibers, and survival times of hypertension patients, and it is evident that the RL-Weibull, which is a special case of the RL-G family, outperformed its submodels and other distributions
- ItemDoes the data tell the true story? A modelling assessment of early COVID-19 pandemic suppression and mitigation strategies in Ghana(Plose One, 2021) Frempong, Nana Kena; Acheampong, Theophilus; Apenteng, Ofosuhene O.; Nakua, Emmanuel; Amuasi,; 0000-0002-7138-3526This paper uses publicly available data and various statistical models to estimate the basic reproduction number (R0) and other disease parameters for Ghana’s early COVID-19 pan demic outbreak. We also test the effectiveness of government imposition of public health measures to reduce the risk of transmission and impact of the pandemic, especially in the early phase. R0 is estimated from the statistical model as 3.21 using a 0.147 estimated growth rate [95% C.I.: 0.137–0.157] and a 15-day time to recovery after COVID-19 infection. This estimate of the initial R0 is consistent with others reported in the literature from other parts of Africa, China and Europe. Our results also indicate that COVID-19 transmission reduced consistently in Ghana after the imposition of public health interventions—such as border restrictions, intra-city movement, quarantine and isolation—during the first phase of the pandemic from March to May 2020. However, the time-dependent reproduction number (Rt) beyond mid-May 2020 does not represent the true situation, given that there was not a consistent testing regime in place. This is also confirmed by our Jack-knife bootstrap esti mates which show that the positivity rate over-estimates the true incidence rate from mid May 2020. Given concerns about virus mutations, delays in vaccination and a possible new wave of the pandemic, there is a need for systematic testing of a representative sample of the population to monitor the reproduction number. There is also an urgent need to increase the availability of testing for the general population to enable early detection, isolation and treatment of infected individuals to reduce progression to severe disease and mortality
- ItemGeneralization of Odd Ramos-Louzada generated family of distributions: Properties, characterizations, and applications to diabetes and cancer survival datasets(Elsevier, 2024) Okutu, John Kwadey; Frempong, Nana Kena; Appiah, Simon K.; Adebanji, Atinuke O.; 0000-0002-7138-3526Probability distributions offer the best description of survival data and as a result, various lifetime models have been proposed. However, some of these survival datasets are not followed or suf ficiently fitted by the existing proposed probability distributions. This paper presents a novel Kumaraswamy Odd Ramos-Louzada-G (KumORL-G) family of distributions together with its statistical features, including the quantile function, moments, probability-weighted moments, order statistics, and entropy measures. Some relevant characterizations were obtained using the hazard rate function and the ratio of two truncated moments. In light of the proposed KumORL-G family, a five-parameter sub-model, the Kumaraswamy Odd Ramos-Louzada Burr XII (KumORLBXII) distribution was introduced and its parameters were determined with the maximum likelihood estimation (MLE) technique. Monte Carlo simulation was performed and the numerical results were used to evaluate the MLE technique. The proposed probability distribu tion’s significance and applicability were empirically demonstrated using various complete and censored datasets on the survival times of cancer and diabetes patients. The analytical results showed that the KumORLBXII distribution performed well in practice in comparison to its sub models and several other competing distributions. The new KumORL-G for diabetes and cancer survival data is found extremely efficient and offers an enhanced and novel technique for modeling survival datasets.
- ItemModelling the volatility of the Ghana stock market: A comparative study(International Journal of Statistics and Applied Mathematics, 2023) Agyarko, Kofi; Wiah, Eric Neebo; Frempong, Nana Kena; Odoi, Benjamin; 0000-0002-7138-3526The Ghana stock market is considered attractive to both local and international investors, as it is a developing market with potential for growth. The volatility of stock returns is one of the crucial features of Ghana's stock market that should be carefully taken into account by any investor or policymaker. As a result, the GARCH, TGARCH, and EGARCH models were used in this study to analyze the volatility of the Ghanaian stock market. The models were assessed using Akaike Information Criterion (AIC), RMSE and MAPE. The TGARCH (1,1) with generalized error distribution was the model that suited the data the best based on the AIC, RMSE, and MAPE values.
- ItemPredictive Maintenance Model Based on Multisensor Data Fusion of Hybrid Fuzzy Rough Set Theory Feature Selection and Stacked Ensemble for Fault Classification(Hindawi, 2022) Buabeng, Albert; Simons, Anthony; Frempong, Nana Kena; Ziggah, Yao Yevenyo; 0000-0002-7138-3526With the rising demand for integrated and autonomous systems in the eld of engineering, e cient frameworks for instant detection of performance anomalies are imperative for improved productivity and cost-e ectiveness. is study proposes a systematic predictive maintenance framework based on the hybrid multisensor fusion technique of fuzzy rough set feature selection and stacked ensemble for the e cient classi cation of fault conditions characterised by uncertainties. First, a feature vector of time-domain features was extracted from 17 multiple sensor signals. en, a comparative study of six di erent Fuzzy Rough Set Feature Selection (FRFS) methods was employed to select the various combinations of optimal feature subsets for various faults classi cation tasks. e determined optimal feature subsets then served as inputs for training the stacked ensemble (ESB(STK)). In the ESB(STK), Support Vector Machine (SVM), Multilayer Perceptron (MLP), k-Nearest Neighbour (k-NN), C4.5 Decision Tree (C4.5 DT), Logistic Regression (LR), and Linear Discriminant Analysis (LDA) served as the base classi ers while the LR was selected to be the metaclassi er. e proposed hybrid framework (FRFS-ESB(STK)) improved the classi cation accuracy with the selected combinations of optimal feature subset size whiles reducing the computational cost, over tting, training runtime, and uncertainty in modelling. Overall analyses showed that the FRFS-ESB(STK) proved to be generalisable and versatile in the classi cation of all conditions of four monitored hydraulic components (i.e., cooler, valve, accumulator, and internal pump leakage) when compared with the six base classi ers (standalone) and three existing ensemble classi ers (Stochastic Gradient Boosting (SGB), Ada Boost (ADB), and Bagging (BAG)). e proposed FRFS-ESB(STK) showed an average improvement of 11.28% and 0.88% test accuracies when classifying accumulator and pump conditions, respectively, whiles 100% classi cation rates were obtained for both cooler and valve.