Department of Statistics
Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/4862
Browse
94 results
Search Results
Item Modeling the Relationship between Maternal Blood Type, and Pregnancy Complications and Delivery Outcome through Moderated Mediation Analysis(University of Ghana, 2023) Boateng, A.F.Pregnancy complications and adverse delivery outcome are of global concern, yet, their causal mechanism(s) are unknown. Although, there have been studies to expound the importance of maternal blood type in pregnancy, and to associate pregnancy complications with maternal blood type, unfortunately, only direct relationships have been assessed. This study employs antepartum and intrapartum risk scores, to give a fuller picture of the complex causal relationship between maternal blood type and pregnancy complications and delivery outcomes, through a moderated mediated relationship, and a double-mediated relationship respectively. Methods: This was a retrospective study of the maternal delivery records of Battor Catholic Hospital in the Volta region of Ghana. The antepartum, intrapartum, and neonatal variables from the maternal delivery records book were extracted and together with the maternal life status, these variables were converted into antepartum and intrapartum risk scores and pregnancy delivery outcome scores, according to the degree of their adverse effect on the life of mother and child. Pregnancy complications were converted into pregnancy complications scores, according to their prevalence and case fatality rates. Mediated and moderated mediated models, were employed in R to analyze the data. The index of moderated mediation between maternal blood type and pregnancy complications outcome was significant (effect = 0.14, BootSE = 0.04 and CI = (0.07, 0.24)) and the indirect effects (when HIPRSI = 0, effect = -0.071, BootSE = 0.02 and CI = (-0.11, -0.04)) of maternal blood type through the risk of pregnancy and delivery was also significant. The double-mediated relationship between maternal blood type and pregnancy delivery outcome was significant (effect = 0.17, se = 0.05 and p-value < 0.001). In conclusion, maternal blood type is the potential cause of pregnancy complications and adverse delivery outcomeItem Modeling Large Insurance Claims Using Extreme Value Theory: A Case Study Of The 37 Military Hospital.(University Of Ghana, 2020) Collins, A.The private health insurance industry is one of the vital components in nation-building. It complements government’s efforts in reducing “out-of-pocket” payment for healthcare services in the country. However, some private health insurance companies face severe insolvency issues due to accumulation of unanticipated huge claim amounts. The Extreme Value Theory (EVT) is a statistical tool proven to help solve or mitigate some of these challenges since it focuses mainly on the behaviour of severe but rare occurrence. In this study, we employ the EVT approaches to model large insurance claims from the 37 Military hospital; and to estimate financial risk indicators such as Value-at-Risk (VaR) and Expected Shortfall (ES) among other extreme quantiles. Conclusions drawn from analysis established that the Weibull class of distributions is more appropriate for the data at hand and for this reason, it is not likely for the 37 Military hospital to submit claim amount exceeding 24,618 cedis for any given day. In addition, private health insurance firms can be assured at a confidence level of 99%, 99.5% and 99.9% that within a day, the hospital is not likely to submit a claim amount exceeding 2,910 cedis, 3,938 cedis and 7,946 cedis respectively. Finally, it was recommended that the NHIA could replicate this study using the claims received by the public health insurance scheme (i.e. NHIS) since it can go a long way to strengthen the financial sustainability of the scheme.Item Survival Analysis Among Tuberculosis Patients: A Case Study of Adults in Kano State in Nigeria(University Of Ghana, 2022-05) Adamu, I.Tuberculosis (TB) is an infectious disease that has been considered as a signi_- cant risk factor that causes ill health. Globally, it has been found to be among the top 10 causes of death and ranks above HIV/AIDS as a single infectious agent that causes death in patient. Many researches have been documented using semiparametric and non-parametric models to analyze survival data in Nigeria. There is dearth of studies on the use of parametric models on tuberculosis survival data. Parametric models such as Weibull, Exponential, Log-logistic, Gompertz etc have been used in various studies to analyze data and Weibull was mostly found to be suitable. The popular non-parametric and semi-parametric tests used in various studies include the K-M, Log rank and Cox Proportional hazard model. However, necessary diagnostic checks on model _tness and non-violation of assumptions were mostly ignored. This reduces the reliability of result and increase chance of estimation error. This study assessed the parametric and semi-parametric model of survival such as Cox Model, Weibull, Exponential and Gompertz Models. A retrospective cohort analysis was conducted on the tuberculosis patients receiving treatment under the Tuberculosis & Leprosy Control Program in Kano, Nigeria. The risk factors for death were assessed using the Cox proportional hazard model. The risk factors for death were assessed using the Cox proportional hazard model. The parametric models were compared, and the gompertz model was found to be the best _t for the data based on its minimum AIC & log-likelihood value. Among 2,555 the TB cases, the success rate of TB treatment was 97.06% and the mortality rate was 2.94%. Multivariate analysis showed that HIV, Age & Weight were signi_cant factors associated with mortality in TB patients during therapy. The study recommends the use of diagnostic checks such as Martingale, Deviance Residuals in model _tness. Also, comparism of parametric models is recommended in determination of best model that _ts tuberculosis data of patients. Key words: Survival Analysis, Kaplan Meier, Cox Proportional Hazard Model, Parametric Models, Tuberculosis.Item Modelling The Impact Of Political Stability On Cocoa Production(University Of Ghana, 2019-12) Oforiwaa, P.Economic growth and political stability are genuinely interrelated. In Ghana, the Cocoa Production Sector is one of the main boosters of the GDP. This paper used political stability as major intervention on the cocoa production. It sought to estimate and assess the impact of Political Stability as a variation on Cocoa Production in Ghana using Bia and Tiao, intervention analysis model. Time series data on cocoa productions from the department of Monitoring, Research and Evaluation of Ghana COCOBOD spanning from the year 1968 to 2016 was used. The Empirical result indicates that, the pre- intervention period was modeled with ARIMAX process based on which the full intervention model was obtained. The intervention event exists but it has an insignificant impact on cocoa production. The Ljung- Box test and its residual plots were significant. It concluded that the insignificant of political stability on cocoa production means that there is no influence of political appointees on the cocoa production. The study recommends that, the cocoa production sector should be independent of political interference since it’s the back bone of Ghana’s GDP.Item Credit Card Fraud Detection; A Machine Learning Approach(University Of Ghana, 2020-11) Glah, J.In recent times, credit card usage has increased tremendously because it is convenient to use and also saves a lot of time. Credit cards are rectangular plastic cards issued by banks which allow a person to borrow funds from a pre - approved limit to pay for one’s purchases now and pay later. In the same manner, credit card frauds have also been on the increase causing huge sums of financial loss to credit card issuers. Credit card fraud is the use of a credit card by someone who is not the owner of the card and is not allowed to use it. In this study, three classification methods were used to do a deep analysis of credit card transactions history and the fraud detection models built. This study presents and demonstrates the advantages of support vector machine, artificial neural network and the k - nearest neighbor algorithms to the credit cards data for the purpose of reducing the bank’s losses. The results show that the linear support vector machine and k - nearest neighbor approaches outperform artificial neural network in solving the problem under investigation. This study allows for multiple algorithms to be integrated together as modules and their results combined to increase the accuracy of the final results.Item Determining Premium In An Excess-Of-Loss Reinsurance Contract -An Extreme Value Approach(University Of Ghana, 2022-06) Adams, S.Statistics of extremes deals with the estimation of rare events that may have catastrophic effects on life, environment, among others. Since the introduction of Extreme value theory (EVT), it has been used in modelling various extreme events in fields such as finance, insurance, transportation, etc. In this thesis, the EVT is applied to model two claims datasets from the Ghanaian insurance industry. To do this, we employ the Peak Over Threshold (POT) method using the splicing Generalized Pareto Distribution (GPD) in modelling the tails of the underlying distributions. The primordial parameter in the estimation of extreme events is the tail index or Extreme Value Index (EVI). The EVI enables the classification of the underlying distribution of a dataset into three family of distributions that have short, light, or heavy tails. Thereafter, any of the parameters of extremes such as extreme quantiles, small exceedance probabilities, right endpoints and return periods can be estimated. Excess Loss Premium (XLP), Expected Shortfall (ES) and Value at Risk (VaR) as risk measures were thereafter calculated through the splicing method. The impact of the extreme value index (EVI) on these risk measures for the two datasets are discussed and suggestions made on how these could help the primary insurer in limiting the danger of large claims on the solvency of these companies. Based on this, the insurance companies can assess the risk associated with large claims and transfer some of these risks to reinsurance companies given their retention level. This study recommends that the splicing method should be used in fitting insurance data which behaves differently at various intervals of claims amount.Item An Investigation Into Modeling Non-Life Insurance Claims In The Nigerian And Ghanaian Insurance Market(University Of Ghana, 2022-04) Ringim, M.N.For calculating non-life insurance premiums, actuaries rely on separate modeling of frequency and severity using covariates to explain the claims loss exposure. In this thesis, we focus on the insurance claims severity amount. Two separate insurance claims data were analyzed using some selected Tree-Based Machine Learning (ML) Algorithms namely; the Classification and Regression Tree (CART), Random Forest (RF), and Gradient Boosting (GB) Models. The predictive performance of the selected models were compared using the Coefficient of determination (R2), Mean Absolute Error (MAE), and Mean Squared Error (MSE). In the application of the selected models, this Thesis relied on two different insurance claims data; The Nigerian and the Ghanaian Insurance claims dataset. The Nigerian dataset had 10,017 observations from paid claims with 4 explanatory variables, while the Ghanaian dataset had 5,495 observations with 7 explanatory variables. In the analysis, 70% of the data were used for training and 30% for testing. Both datasets were compared in terms of the selected performance measures. The results show that the Random Forest model of the claims amount had the overall best performance for both Nigerian and Ghanaian Dataset.Item Modelling Covid-19 Transmission In Ghana Using A Discrete-Time Markov Model And Machine Learning Time-Series Forecasting Algorithms(University Of Ghana, 2022-09) Koduah, P.P.The COVID-19 pandemic has and continue to have a severe impact on the health sectors, businesses, economies, and the world at large, despite many healthcare interventions, with much still yet to be learnt regarding its infection dynamics. In addition, researchers have developed classical compartmental or epidemiological models and other advanced mathematical models to better explain COVID- 19 infection dynamics across many countries. Critical information, such as the likelihood of first infection and recovery, average infection duration before this infection dies out entirely, COVID-19 infected people's life expectancy, and generalised transition probabilities, is understudied at any given future time. Using nationwide aggregated COVID-19 datasets and a discrete-time Markov model (to estimate these key disease metrics), the current study adds to our understanding of COVID-19 infection dynamics in Ghana. Additionally, the predictive power of some existing state-of-the-art machine learning (ML) algorithms such as K-Nearest Neighbor regression (KNN), Neural Network Auto-Regressive (NNAR), Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP), and Extreme Learning Machines (ELM) in forecasting daily cases of COVID-19 infection (over the study period) is investigated using an out-of sample rolling-origin evaluation by exploring the trade-o_ between computational speed and accuracy. It was estimated that there would be a prolonged COVID- 19 transmission for at least 150 years before infection could die out. The study supports the idea that with a high overall recovery rate, a low infection rate, and a longer infection period, there is a possibility of herd immunity (as evident in the 2021 infection period despite the relatively high overall rate of infection). Finally, the K-Nearest Neighbour (KNN) regression was found to be the most cost-effective ML algorithm to predict the daily cases of COVID-19 in Ghana via the rolling-origin evaluation strategy.Item Adopting Zero Inflated Models For Claim Counts And The Gamma Regression Model For Claims Cost In Determining Actuarial Premiums(University Of Ghana, 2022-04) Amenu, F.M.Insurance is the exchange of risk by an insured person through the payment of premiums for financial protection and economic benefit. The problem is how premiums should be charged so as to keep the industry alive to perform this basic function of insurance. Because of the Bonus-Malus system, or Hunger for Bonus system (also called No Claim Discount), and deductibles, most claims are not reported by policyholders, causing the number of claims to be dominated by zeros, which leads to over-dispersion in the data. In modeling the claim frequency, the Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models were adopted. The Gamma regression model was used to fit the claims cost data. The claim frequency regression model that best fits the claim frequency with the Gamma model for the claims cost was combined in determining the actuarial premium. These models were numerically illustrated with data obtained from a major non-life insurance company in Ghana and French Motor Third- Party Liability data from https://www.kaggle.com/datasets/karansarpal/ fremtpl2-french-motor-tpl-insurance-claims. The score test demonstrated the inability of the Poisson model to appropriately model the claims data due to the inflation of zeros in the data. The ZIP and ZINB were both found to be superior to their conventional equivalents based on the Vuong test statistics. The ZIP was chosen as an appropriate model for analyzing claim frequency data for both the French and Ghanaian data based on the values of the AIC and BIC. The risk factors that were found to influence claim frequency and claim cost were discovered to be different when both datasets were used. It is recommended that a separate analysis of claim frequency and claim cost be conducted with claim frequency receiving a high rating power.Item Modelling Insurance Attrition Using Survival Analysis – A Case Study Of Ghana(University Of Ghana, 2022-05) Asare, M.J.Life insurance operations immensely contributes to the economic growth and development of a nation while also serving as an alternative form of internal fund mobilization for developing economies. This notwithstanding, life insurance companies tend to face challenges. One of these challenges they face is insurance attrition. This condition arises when insurance policies are terminated by the insurer as a result of discontinuation of premium payment after a specified period of time called the grace period, and also by the policy holder. Many factor(s) contribute to insurance attrition. The study focused on the length of survival time to attrition and the covariates that are likely to influence attrition. Randomly selected data was used in the study. Data was provided by an insurance company in Ghana for the period May 2018 to April 2021. The study employed Kaplan-Meier estimators, log-rant test and Cox regression model for the analysis of data. The study revealed the survival time of a new client is 16 weeks after subscribing on to policy. It also revealed assuming a three year period, attrition will occur after 15 weeks of being in force. The study concludes that marital status, product type, base rate change, deduction source are the factors that influence insurance attrition in Ghana.