Department of Statistics
Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/34524
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Item Bayesian and Multilevel Approaches to Modelling Road Traffic Fatalities in Ghana(2016-07-04) Hesse, C.A.; Iddi, S.The knowledge of accident rates in a country provides a useful tool for a comprehensive analysis of their causes as well as for their prevention. Prof. R. J. Smeed, in 1949, gave a regression model for estimating road traffic fatalities. The study gives a modified form of Smeed’s regression formula for estimating road traffic fatalities in Ghana. The modified regression model was found to be relatively more accurate form for estimating road traffic fatalities in Ghana, than Smeed’s formula. Bayesian model for predicting the annual regional distribution of the number of road traffic fatalities in Ghana is derived, using road traffic accident statistics data from the National Road Safety Commission, Ghana Statistical Service and Driver and Vehicle Licensing Authority. The data span 1991 to 2009. Since the parameters are assumed to vary across the various regions, they are considered to be random variables with probability distributions. The Markov chain Mont Carlo (MCMC) sampling techniques were used to draw samples from each of the posterior distribution, thereby determining the values of the unknown parameters for each region based on a given data. The study has shown that population and number of registered vehicles are predominant factors affecting road traffic fatalities in Ghana. The effect of other additional factors on road traffic fatality such as human (the driver, passenger and pedestrian), vehicle (its condition and maintenance), environmental/weather and nature of the road cannot be ruled out. Similar to the Bayesian model, where the regression parameters are considered as random variables, a Multilevel Random Coefficient (MRC) model for predicting road traffic fatalities in Ghana was also developed. In this model, the number of road traffic fatalities and the regional groups are conceptualized as a hierarchical system of road traffic fatalities and geographical regions of Ghana, with fatalities and regions defined at separate levels of this hierarchical system. Instead of estimating a separate regression equation for each of the 10 regions in Ghana, a multilevel regression analysis was applied to estimate the values of the regression coefficients for each region based on data given.Item Comparison of Least Squares Method and Bayesian with Multivariate Normal Prior in Estimating Multiple Regression Parameters(2016-02-17) Mettle, F.O.; Iddi, S.Based on an assumption of multivariate normal priors for parameters of multivariate regression model, this study outlines an algorithm for application of traditional Bayesian method to estimate regression parameters. From a given set of data, a Jackknife sample of least squares regression coefficient estimates are obtained and used to derive estimates of the mean vector and covariance matrix of the assumed multivariate normal prior distribution of the regression parameters. Driven to determine whether Bayesian methods to multivariate regression parameter estimation present a stable and consistent improvement over classical regression modeling or not, the study results indicate that the Bayesian method and Least Squares Method (LSM) produced almost the same estimates for the regression parameters and coefficient of determination with the Bayesian method having smaller standard errors.Item Modified zero-inflated negative binomial model with application to clinical trial in epileptic patients(2016-03-10) Iddi, S.Marginalized models are in great demand by many researchers in the life sciences, particularly in clinical trials studies, epidemiology, health-economics, surveys and many others, since they allow generalization of inference to the entire population under study. For count data, standard procedures such as the Poisson regression and negative binomial model provide population average inference for model parameters. However, occurrence of excess zero counts and lack of independence in empirical data have necessitated their extension to accommodate these phenomena. These extensions, though useful, complicate interpretations of fixed effects. For example, the zero-inflated Poisson model accounts for the presence of excess zeros, but the parameter estimates do not have a direct marginal inferential ability as the base model, the Poisson model. Marginalizations due to the presence of excess zeros are underdeveloped though demand for them is interestingly high. The aim of this talk, therefore, is to present a developed marginalized model for zero-inflated univariate count outcome in the presence of overdispersion. Emphasis is placed on methodological development, efficient estimation of model parameters, and application to a clinical trial dataset. Results of simulation study performed to assess the performance of the model are also discussed.