Department of Statistics
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Item 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 The relative efficiency of time-to-progression and continuous measures of cognition in presymptomatic Alzheimer’s disease(Alzheimer's & Dementia: Translational Research & Clinical Interventions, 2019-07-18) Iddi, S.; Li, D.; Aisen, P.S.; Thompson, W.K.; Donohue, M.C.Introduction: Clinical trials on preclinical Alzheimer’s disease are challenging because of the slow rate of disease progression.We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression. Methods: Multivariate continuous data are simulated from a Bayesian joint mixed-effects model fit to data from the Alzheimer’s Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. Results: We find that power is approximately doubled with models of repeated continuous outcomes compared with the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained. Discussion: Given the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer’s disease.Item Comparison of confidence interval estimators: An index approach(Journal of Applied Probability and Statistics, 2019-04) Minkah, Richard; De wet, TertiusIn many statistical problems, several estimators are usually available for interval estimation of a parameter of interest, and hence, the selection of an appropriate estimator is important. The criterion for a good estimator is to have a high coverage probability close to the nominal level and a shorter interval length. However, these two concepts are in opposition to each other: high and low coverages are associated with longer and shorter interval lengths respectively. Some methods, such as bootstrap calibration, modify the nominal level to improve the coverage and thereby allow the selection of intervals based on interval lengths only. Nonetheless, these methods are computationally expensive. In this paper, we propose an index which offers an easy to compute approach of comparing confidence interval estimators based on a compromise between the coverage probability and the confidence interval length. We illustrate that the confidence interval index has range of values within the neighbourhood of the range of the coverage probability, [0,1]. In addition, a good confidence interval estimator has an index value approaching 1; and a bad confidence interval has an index value approaching 0. A simulation study was conducted to assess the finite sample performance of the index. The proposed index is illustrated with a practical example from the literatureItem 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.Item Predicting the course of Alzheimer’s progression(SpringerOpen, 2019-06-17) Iddi, S.; Li, D.; Aisen, P.S.; Rafii, M.S.; Thompson, W.K.; Donohue, M.C.Alzheimer’s disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer’s dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer’s Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only.Item 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 PressItem Survival Pattern of First Accident among Commercial Drivers in the Greater Accra Region of Ghana(Accident Analysis and Prevention, 2017-06) Nanga, S.; Odai, N.A.; Lotsi, A.In this study, the average accident risk of commercial drivers in the Greater Accra region of Ghana and its associated risks were examined based on a survey data collected using paper-based questionnaires from 204 commercial drivers from the Greater Accra Region of Ghana. The Cox Proportional Hazards Model was used for multivariate analysis while the Kaplan-Meier (KM) Model was used to study the survival patterns of the commercial drivers. The study revealed that the median survival time for an accident to happen is 2.50 years. Good roads provided a better chance of survival than bad roads and experienced drivers have a better chance of survival than the inexperienced drivers. Age of driver, alcohol usage of driver, marital status, condition of road and duration of driver's license were found to be related to the risk of accident.Item Effect of Covariate Misspecifications in the Marginalized Zero-Inflated Poisson Model(Monte Carlo Methods and Applications, 2017-01) Iddi, S.; Nwoko, E.O.Count outcomes are often modelled using the Poisson regression. However, this model imposes a strict mean-variance relationship that is unappealing in many contexts. Several studies in the life sciences result in count outcomes with excessive amounts of zeros. The presence of the excess zeros introduces extra dispersion in the data which cannot be accounted for by the traditional Poisson regression. The zero-inflated Poisson (ZIP) and zero-inflated negative binomial models are popular alternative. The zero-inflated models comprise two key components; a logistic part which models the zeros, and a Poisson component to handle the positive counts. Both components allow the inclusion of covariates. Civettini and Hines [3] investigated misspecification effects in the zero-inflated negative binomial regression models. Long,Preisser, Herring and Golin [10] proposed a so-called marginalized zero-inflated Poisson (MZIP) model that allows direct marginal interpretation for fixed effect estimates to overcome the often sub-population specific interpretation of the traditional zero-inflated models. In this research, the effects of misspecification of components of the MZIP regression model are investigated through a comprehensive simulation study. Two different incorrect specifications of the components of an MZIP model were considered, namely ‘Omission’ and ‘Misspecification’. Bias, standard error (precision) of estimates and mean square error (MSE) are computed while varying the sample size. Type I error rates are also evaluated for the misspecified models. Results of a Monte Carlo simulation are reported. It was observed that omissions in both parts of the models lead to biases in the estimated parameters. The intercept parameters were the most severely affected. Furthermore, in all the types of omissions, parameters in the zero-inflated part of the models were much affected compared to the Poisson part in terms of both bias and MSE. Generally, bias and MSE decrease as sample sizes increase for all parameters. It was also found that misspecification can either increase, preserve or decrease the type I error rates depending on the sample size.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 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.