Department of Statistics
Permanent URI for this collectionhttp://197.255.125.131:4000/handle/123456789/23133
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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 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 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 InitiativeIntroduction 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.Item Endemic grasshopper species distribution in an agro-natural landscape of the Cape Floristic Region, South Africa(Ecological Engineering, 2017) Adu-Acheampong, S.; Samways, M.J.; Landmann, T.; Kyerematen, R.; Minkah, R.; Mukundamago, M.; Moshobane, C.M.Conservation biologists and ecologists often make use of models to identify important biotic and abiotic factors that constrain species distributions for conservation decisions to be taken. In line with such practices, we developed species distribution models for four localized, Cape Floristic Region (CFR) endemic, flightless, congeneric Euloryma grasshopper species. We chose this group as use of these models has been little explored for narrow range endemics with specific traits. Euloryma larsenorum and E. lapollai are associated with fynbos only, while E. umoja and E. ottei are both associated with fynbos and vineyards. We used the Maximum Entropy algorithm, which showed that vegetation type and soil characteristics were the most important environmental factors affecting local distribution of Euloryma species in the CFR. The models also showed that Euloryma species have a very narrow habitat suitability range in the CFR. We also showed that there are no significant differences in the distribution of species associated with fynbos only as well as those associated with both fynbos and vineyards. E. larsenorum and E lapollai are likely to be the most affected species in the event of further habitat transformation from fynbos to agricultural production. This is not likely to be the case for E. umoja and E. ottei which can tolerate agriculture environment, although they might survive both sets of environments in accordance with their life history traits. © 2017 Elsevier B.V.Item Large deviation results for critical multitype galton-watson trees(Far East Journal of Mathematical Sciences, 2017) Doku-Amponsah, K.In this paper, we prove a joint large deviation principle in n for the empirical pair measure and empirical offspring measure of critical multitype Galton-Watson trees conditioned to have exactly n vertices in the weak topology. From this result we extend the large deviation principle for the empirical pair measures of Markov chains on simply generated trees to cover offspring laws which are not treated by [10, Theorem 2.1]. For the case where the offspring law of the tree is a geometric distribution with parameter (Formula presented) we get an exact rate function. All our rate functions are expressed in terms of relative entropies. © 2017 Pushpa Publishing House, Allahabad, India.Item Modelling Vehicular Crash Mortalities in Ghana(Model Assisted Statistics and Applications, 2018) Somua-Wiafe, E.; Asare-Kumi, A.; Nortey, E.N.N.; Iddi, S.Deaths due to road accidents are major concern to many stakeholders in Ghana especially because road accidents only come second behind malaria for cause of deaths. Statistical models can be helpful in evaluating the effect of factors responsible for mortality and morbidity during vehicular accidents. There is often a spoilt for choice on the type of models that may be used to explain a particular phenomenon. Picking a model can be based on the researcher’s knowledge or experience and the simplicity of the model. However, in common applications, the models applied are often not adequate to accurately and efficiently explain underlying phenomenon particularly when it fails to address certain characteristics of the data. In this paper, an appropriate statistical model on the number of vehicular deaths in Ghana is fitted. The Poisson, Negative Binomial (NB), Zero-Inflation Poisson (ZIP) and Zero-Inflation Negative Binomial (ZINB) models, estimated by the method of maximum likelihood, are compared to determine the most appropriate model for the data at hand. In addition, due to the large number of explanatory variables, the backward model selection procedure was adopted to select the most significant factors associated with crash fatalities. After a careful model building process, the ZINB model was identified as the most appropriate for modelling road crash mortality. The model also identified factors such as shoulder type, time of crash, driver’s sex, road environment landmarks, among others as having significant effect on the fatalities during vehicular accidents in Ghana. It is recommended that authorities focus on installing reflective markings on the shoulders of roads and increase education of drivers in adhering to road regulations while also paying keen attention to road environmental landmarks.Item Maternal and neonatal characteristics that influence very early neonatal mortality in the Eastern Regional Hospital of Ghana, Koforidua: a retrospective review(BMC Research Notes, 2018-02) Avoka, J.A.; Adanu, R.M.K.; Wombeogo, M.; Seidu, I.; Dun-Dery, E.J.Objective This study was conducted to determine the maternal and neonatal characteristics that influence very early neonatal mortality using 811 delivery records at the Eastern Regional Hospital of Ghana. Results The very early neonatal mortality rate was 9 per 1000 live births. Multi-parity reduced the odds of very early neonatal death by 30%. Mothers with a previous neonatal death had about 8 times the odds of having a very early neonatal death as compared to those without a history of neonatal death.Item 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).Item 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 InitiativeCharacterization 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.