Department of Statistics

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A methodology for stochastic analysis of share prices as Markov chains with finite states
(2014-11-06) Mettle, F.O.; Quaye, E.N.B.; Laryea, R.A.
Abstract Price volatilities make stock investments risky, leaving investors in critical position when uncertain decision is made. To improve investor evaluation confidence on exchange markets, while not using time series methodology, we specify equity price change as a stochastic process assumed to possess Markov dependency with respective state transition probabilities matrices following the identified state pace (i.e. decrease, stable or increase). We established that identified states communicate, and that the chains are aperiodic and ergodic thus possessing limiting distributions. We developed a methodology for determining expected mean return time for stock price increases and also establish criteria for improving investment decision based on highest transition probabilities, lowest mean return time and highest limiting distributions. We further developed an R algorithm for running the methodology introduced. The established methodology is applied to selected equities from Ghana Stock Exchange weekly trading data.
Acceptance of biotechnology and social-cultural implications in Ghana
(African Journal of Biotechnology, 2009-05) Quaye, W.; Yawson, I.; Yawson, R.M.; Williams, I.E.
Despite major scientific progress in the application of biotechnology in agriculture, public attitudes towards biotechnology in general and genetically modified food (GM food) products in particular remain mixed in Africa. Examining responses on acceptance of GM food through a stakeholder survey in Ghana, it was established that half of the 100 people sample interviewed were not in favor of GM foods. To this group acceptance of GM foods would make farmers loose focus on the traditional ways of cultivation, putting the whole nation at the mercy of profit driven foreign companies who produce GM foods. In order to have clear and unbiased attitudes towards agricultural biotechnology in Africa, there is the need to substitute dominant ideologies in the way biotechnology research and dissemination are conducted in developed countries with tailor-made methodologies in developing countries. This paper emphasizes the social dynamic force of food focusing on the need for social shaping of biotechnologies to reflect local and regional needs. Respondents' perceptions of GM foods suggest that food is seen as not just a commodity to be consumed but food has both cultural and national identities. Generally, people are identified by their consumption and nutrition lifestyles and therefore take pride in what they eat. A proposal is made to set biotechnology research agenda in the context of social choices; social scientific coalition of biotechnology with endogenous development pathways' as opposed to 'exogenous biotechnology research'. Also there is the need for adequate capacity building of the existing regulatory institutions to handle ethical and moral issues associated with biotechnology research since survey findings showed lacked of public confidence in them. © 2009 Academic Journals.
Analysis of Exchange Rates as Time-Inhomogeneous Markov Chain with Finite States
(Journal of Applied Mathematics, 2022) Mettle, F.O.; Boateng, L.P.; Quaye, E.N.B.; Aidoo, E.K.; Seidu, I.
Irrespective of whether the test for homogeneity is significant or not, most researchers assume time-homogeneity in analysing Markov chains due to scanty literature on the analysis of time-inhomogeneous Markov chains. Based on the assumption that, for each point in time in the future, a stochastic process will be subjected to a randomly selected transition matrix from an ergodic set of transition matrices the process was subjected to in the recent past, a methodology was proposed for analysing the long-run behaviours of time-inhomogeneous Markov chains. The proposed model was implemented to historical data consisting of the exchange rate of cedi-dollar, cedi-pound, and cedi-euro spanning over 6 years (January 2012 to December 2017). The results show that under certain “closeness” conditions, the long-run behaviours of the time-inhomogeneous case are almost identical to those of the time-homogeneous case. The paper asserted that even if the Markov chain exhibit time-inhomogeneity, analysing the Markov chain under the assumption of time-homogeneity is a step in the right direction under certain “closeness” conditions; otherwise, the proposed method is recommended. It was also found that investing in dollars yields better returns than the other currencies in Ghana.
Analysis of Investment Returns as Markov Chain Random Walk
(International Journal of Mathematics and Mathematical Sciences, 2024) Mettle, F.O.; Agyekum, L.; Aidoo, E.K.; Dowuona, C.O.N.
The main objective of this paper is to analyse investment returns using a stochastic model and inform investors about the best stock market to invest in. To this effect, a Markov chain random walk model was successfully developed and implemented on 450 monthly market returns data spanning from January 1976 to December 2020 for Canada, India, Mexico, South Africa, and Switzerland obtained from the Federal Reserves of the Bank of St. Louis. Limiting state probabilities and six-month moving crush probabilities were estimated for each country, and these were used to assess the performance of the markets. Te Mexican market was observed to have the lowest probabilities for all the negative states, while the Indian market recorded the largest limiting probabilities. In the case of positive states, the Mexican market recorded the highest limiting probabilities, while the Indian market recorded the lowest limiting probabilities. The results showed that the Mexican market performed better than the others over the study period, whilst India performed poorly. These findings provide crucial information for market regulators and investors in setting regulations and decision-making in investment.
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.
Application of Markov Chain Techniques for Selecting Efficient Financial Stocks for Investment Portfolio Construction
(Journal of Applied Mathematics, 2022) Kallah-Dagadu, G.; Apatu, V.; Mettle, F. O.; Arku, D.; Debrah, G.
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.
Approximate and Exact Optimal Designs for Paired Comparison Experiments
(Calcutta Statistical Association Bulletin, 2022) Nyarko, E.; Doku-Amponsah, K.
In this article, the problem of finding optimal paired comparison approximate and exact designs for the identification of main effects and two and three and four attribute interactions, when the alternatives are characterized by either full profiles or partial profiles, is considered. The resulting designs are also optimal under the indifference assumption of equal choice probabilities for a multinomial logit model when the choice sets are pairs.
Assessing Patient Satisfaction And Some Related Factors In The Kasena Nankana District-Ghana
(International Journal of Scientific and Technology Research, 2018-12) Affi, P.O.; Duah, K.O.; Oppong, I.
To access the relationship between patient satisfaction and some contributing factors, a study was conducted on 200 patients from the War Memorial Hospital. 54% of the patients were males whilst 46% were females. About 67% of the patients were satisfied meaning the satisfaction level at the hospital is higher. A logistic regression model was developed to establish a relationship between patient satisfaction and some contributing factors (age, sex, education, job, health, LTIME, AESTH, PHWR and NHIS). The result indicates that the most important variables associated with patient satisfaction are Sex, LTIME (length of time in attaining services), AESTH (aesthetic features) and PHWR (Patient health-worker relationship).
Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm
(Hindawi, 2021) Asiedu, L.; Mensah, J.A.; Ayiah-Mensah, F.; Mettle, F.O.
'e drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environments with poor illuminations, varying head poses, ageing, facial expressions, and occlusions. Although several researchers have leveraged on the property of bilateral symmetry to reconstruct half-occluded face images, their approach becomes deficient in the presence of random occlusions. In this paper, we harnessed the benefits of the multiple imputation by the chained equation technique and image denoising using Discrete Wavelet Transforms (DWTs) to reconstruct degraded face images with random missing pixels. Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. 'e study also revealed that the average recognition rate for the augmented face images (75.5811) was significantly lower than the average recognition rate (430.7153) of the occluded face images. MICE augmentation is recommended as a suitable data enhancement mechanism for imputing missing data/pixel of occluded face images.
Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints
(Hindawi, 2021) Mensah, J.A.; Asiedu, L.; Mettle, F.O.; Iddi, S.
Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.
Assessment of Neonatal Mortality and Associated Hospital-Related Factors in Healthcare Facilities Within Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana
(Health Services Insights, 2024) Tawiah, K.; Asosega, K.A.; Iddi, S.; et al.
Objectives: Ghana’s quest to reduce neonatal mortality, in hospital facilities and communities, continues to be a nightmare. The pursuit of achieving healthy lives and well-being for neonates as enshrined in Sustainable Development Goal three lingered in challenging hospital facilities and communities. Notwithstanding that, there have been increasing efforts in that direction. This study examines the contributing factors that hinder the fight against neonatal mortality in all hospital facilities in the Sunyani and Sunyani West Municipal Assemblies in Bono Region, Ghana. Methods: The study utilized neonatal mortality data consisting of neonatal deaths, structural facility related variables, medical human resources, types of hospital facilities and natal care. The data was collected longitudinally from 2014 to 2019. These variables were analysed using the negative binomial hurdle regression (NBH) model to determine factors that contribute to this menace at the facility level. Cause-specific deaths were obtained to determine the leading causes of neonatal deaths within health facilities in the two municipal assemblies. Results: The study established that the leading causes of neonatal mortality in these districts are birth asphyxia (46%), premature birth (33%), neonatal sepsis (11%) and neonatal jaundice (7%). The NBH showed that neonatal mortality in hospital facilities depend on the num ber of incubators, monitoring equipment, hand washing facilities, CPAPb machines, radiant warmers, physiotherapy machines, midwives, paediatric doctors and paediatric nurses in the hospital facility. Conclusions: Early management of neonatal sepsis, birth asphyxia, premature birth and neonatal infections is required to reduce neonatal deaths. The government and all stakeholders in the health sector should provide all hospital facilities with the essential equipment and the medical human resources necessary to eradicate the menace. This will make the realization of Sustainable Development Goal three, which calls for healthy lives and well-being for all, a reality.
Assessment of the determinants that influence the adoption of sustainable soil and water conservation practices in Techiman Municipality of Ghana
(International Soil and Water Conservation Research, 2019) Darkwah, K.A.; Kwawu, J.D.; Agyire-Tettey, F.; Sarpong, D.B.
This paper assesses the relationship between farmer characteristics and the degree to which nine soil and water conservation practices (SWCPs) are adopted by 300 maize farmers in Techiman Municipality, Ghana. Farmers were surveyed for their adoption of nine SWCPs, and 24 other characteristics including demographics, socio-economic factors, risk factors and costs of production. Sustainable soil and water conservation practices (SWCPs) in sub-Saharan Africa such as Ghana are important because they have positive effects on yield, increase sustainability of farming, stop degradation and reduce soil erosion. The adoption of sustainable soil and water conservation practices in the agricultural industry of Ghana has been variable. This study aims to explain differences in farmer adoption of SWCPs, by assessing factors that vary with the number of different SWCPs used by farmers. Hence, the Poisson model was used. The results show that farmer's household size, farm size, access to credit services and formal training of maize farmer have a positive significant association with the number of soil and water conservation practices adopted by maize farmers while distance to nearest output market, distance to input center, access to extension services, and risk of pest and diseases have a negative significant association with the number of soil and water conservation practices adopted by maize farmers at 5% significance level. The study concludes that any further research in Techiman Municipality on soil and water conservation practices should acknowledge the mixture of personal and demographic, institutional, socio-economic and risk factors. This suggests that agricultural policies formulated by the government should be aimed at supporting maize farmers to have access to extension service contact for frequent disseminating of agricultural technology information which is likely to increase the rate of adoption of soil and water conservation practices. © 2019 International Research and Training Center on Erosion and Sedimentation and China Water and Power Press
Asymptotic Equipartition Properties for simple hierarchical and networked structures
(Cambridge University Press, 2011) Doku-Amponsah, K.
We prove asymptotic equipartition properties for simple hierarchical structures (modelled as multitype Galton-Watson trees) and networked structures (modelled as randomly coloured random graphs). For example, for large n, a networked data structure consisting of n units connected by an average number of links of order n / log n can be coded by about H × n bits, where H is an explicitly defined entropy. The main technique in our proofs are large deviation principles for suitably defined empirical measures.
Asymptotics of the Partition Function of Ising Model on Inhomogeneous Random Graphs
(Far East Journal of Mathematical Sciences, 2017-12) Doku-Amponsah, K.
For a finite random graph, we defined a simple model of statistical mechanics. We obtain an annealed asymptotic result for the random partition function for this model on finite random graphs as $n,$ the size of the graph is very large. To obtain this result, we define the \emph{ empirical bond distribution}, which enumerates the number of bonds between a given couple of spins, and \emph{ empirical spin distribution}, which enumerates the number of sites having a given spin on the spinned random graphs. For these empirical distributions we extend the large deviation principle(LDP) to cover random graphs with continuous colour laws. Applying Varandhan Lemma and this LDP to the Hamiltonian of the Ising model defined on Erdos-Renyi graphs, expressed as a function of the empirical distributions, we obtain our annealed asymptotic result.
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 societies
Bayesian latent time joint mixed effect models for multicohort longitudinal data
(Statistical Methods in Medical Research, 2017-11) Li, D.; Iddi, S.; Thompson, W.K.; Donohue, M.C.; for the Alzheimer's Disease Neuroimaging Initiative
Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson’s and Alzheimer’s, and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference. We apply the model to detailed simulation studies and data from the Alzheimer’s Disease Neuroimaging Initiative.
Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative
(Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, 2018-08) Li, D.; Iddi, S.; Thompson, W.K.; Rafii, M.S.; Aisen, P.S.; Donohue, M.C.; Alzheimer's Disease Neuroimaging Initiative
Introduction We characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative. Methods We apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference. Results We find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis. Discussion The latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms.
Best-worst scaling in studying the impact of the coronavirus pandemic on health professionals in Ghana
(Model Assisted Statistics and Applications, 2023) Nyarko, E.; Arku, D.; Duah, G.
In this study, we utilized a best-worst scaling experiment design to assess the potential factors associated with depression, anxiety, and stress among health professionals following the experience of the COVID-19 pandemic. The maximum difference model was performed to analyze the potential risk factors associated with depression, anxiety, and stress. As a case study, a total of 300 health professionals in Ghana were included in the survey. The majority, 112 (68.7%) male health professionals and 97 (70.8%) female health professionals reported that they had encountered suspected COVID-19 patients. 83 (50.9%) of the male health professionals and 76 (55.5%) of the female health professionals reported that they had encountered confirmed COVID-19 patients. A considerable proportion of the males 59 (36.2%) and females 57 (41.6%) health professionals reported coming into direct contact with COVID-19 lab specimens. The findings indicated that a high proportion of health professionals encountered suspected or confirmed COVID-19 patients, while a considerable proportion had direct contact with COVID-19 lab specimens leading to psychological problems. Risk factors such as contact with confirmed COVID-19 patients, the relentless spread of the coronavirus, death of patients and colleagues, shortage of medical protective equipment, direct contact with COVID-19 lab specimens, and the permanent threat of being infected should be given special attention, and necessary psychological intervention provided for health professionals endorsing these risk factors. Improving the supply of medical protective equipment to meet occupational protection practices, sufficient rest, and improving the vaccination of the population might help safeguard health professionals from depression, anxiety, and stress. Our results provide insight into policy discussions on the mental health of health professionals and interventions that are essential to enhance psychological resilience.
Beyond the green revolution: reviving time-tested resilient practices for enhanced food security in Ghana’s upper west region through traditional Authorities
(Cogent Food & Agriculture, 2024) Dompae, F.; Beyuo, A.; Domanban, P.D.
This paper investigates the effects of locally enacted bylaws governing Autonomous resilience Practices (ARP) on the food security of a sample of 700 smallholder farmers in Ghana’s Upper West Region. The research is grounded in the context of the Green Revolution’s inability to address food insecurity for large populations in Africa. The sequential mixed-methods design employed in the study first identified eight prevalent coping strategies for food insecurity among farmers. A pairwise matrix ranking method was used for this task. Subsequently, Poisson regression models were employed to assess how often farmers resorted to these coping strategies when bylaws aimed at protecting the local ecology were enforced. The results reveal highly significant and inverse relationships between increased frequency of implementing local bylaws on ARP and farmers’ frequency of resorting to the eight identified coping strategies for food security. The results underscore the significance of grassroots-level solutions to the shortcomings of the current food system, which produces surplus food but fails to adequately nourish a substantial proportion of the global population
Canonical Correlation Analysis Of Neonatal Anthropometric Indicators And Maternal Socio-Demographic Factors
(INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, 2019-07) Affi, P.O.; Lartey, L.N.; Angkyiire, D.; Oppong, I.
Abstract : The main objective of this study was to examine the relationship between neonatal anthropometric indicators (Baby head circumference, Baby full length and Baby weight) and maternal socio demographic factors (Gravidity, Parity, Educational level, Age, occupation of mother and number of ANC visit) using canonical correlation analysis. The neonatal anthropometric indicators were considered as depended (Y) variables whiles maternal socio demographic factors were considered as independent variables (X). The outcome of the canonical correlation analysis showed th at, there exists a significant relationship between neonatal anthropometric indicators (Baby head circumference, Baby full length and Baby weigh t) and maternal socio demographic factors (Gravidity, Parity, Educational level, Age, occupation of mother and number of ANC visi t). The total shared variance between the two set of variables was 69. 8 %. The study also showed that Baby head circumference has a positive relationship with maternal Age and also negative relationship with the remaining of the maternal socio demographic f actors whiles Baby full length and Baby weight also has a positive relationship with all the maternal socio demographic factors used in the study except maternal Age.

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