Browsing by Author "Adesina, Olumide Sunday"

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    A Lotka-Volterra Non-linear Differential Equation Model for Evaluating Tick Parasitism in Canine Populations
    (Mathematical Modelling of Engineering Problems, 2023-08-30) Adesina, Olumide Sunday
    This research employs a modified version of the Lotka-Volterra non-linear first-order ordinary differential equations to model and analyze the parasitic impact of ticks on dogs. The analysis reveals that fluctuations in pesticide effects significantly influence tick populations and the size of the canine host. The study also uncovers that alterations in the size of the interacting species can lead to both stable and unstable states. Interestingly, in a pesticide-free environment, a decline in the inter-competition coefficient catalyzes an increase in the sizes of both interacting species. This increase, although marginal for the tick population, contributes to overall system stability. The findings underscore the utility of the Lotka-Volterra non-linear first-order ordinary differential equations in modeling the parasitic effect of ticks on dogs. To protect pets, particularly dogs, from the harmful effects of tick infestation, this study recommends the appropriate and regular application of disinfectants.
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    A modified generalized class of exponential ratio type estimators in ranked set sampling
    (Scientific African, 2023-03-22) Adesina, Olumide Sunday
    Background: Researchers consider ranked set sampling (RSS) as an alternative to simple random sampling (SRS) for data collection because studies have shown that it is more efficient and less biased. Also, introducing population parameters to estimators increases the efficiency of such estimators. Aim: This study derived a modified generalized class of exponential ratio estimator in RSS by introducing available population parameters and compared the results with an existing version in SRS. Methodology: The biases and mean square errors (MSE) of the proposed estimators were derived up to the terms of first-order approximation using Taylor’s series expansion. Efficiency was used as the mode of comparison between the proposed and existing estimators. Results: Life data sets and simulated data supported the numerical illustration to corroborate the theoretical results. Conclusion: The MSEs of the modified generalized class of estimators under RSS were found to be smaller than those of the existing generalized class of estimators under SRS; hence they are more efficient estimators.
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    A non-parametric analysis of the effect of Covid-19 pandemic on Nigerians’ well-being based on geopolitical zones
    (JP Journal of Biostatistics, 2024-02-12) Adesina, Olumide Sunday
    The COVID-19 pandemic has crippled the economic activities of so many nations across the globe since its outbreak in 2019. This study is focused on the consequential effect of the COVID-19 pandemic in terms of standard of living, and perception of economic and security situation of Nigerians. A non-parametric approach was adopted on a primary data obtained through the administration of questionnaires online by NoiPolls during the COVID-19 period. The result obtained from this study depicts that there is a significant relationship between the security situation and perception of the country’s economic situation. Kruskal-Wallis test was used to check if there is a significant difference in economic perception, security situation and standard of living where it was observed that there exists a significant difference based on the geopolitical zone. We, therefore, recommend that efforts should be made by the government towards improving the economic state of affairs especially in the southern part of the country as this will in the long run lead to sustainable cities and communities across the geopolitical zones which is one of the goals of SDGs. Moreover, efforts should also be made by the government towards improving the security situation in the north and southeast as improvement in the country’s economic situation has a direct influence on the security position of the country.
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    Adaptive Models for Tails of Distributions
    (International Journal of Statistics and Economics, 2019) Adesina, Olumide Sunday
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    Adaptive Regression Model for Highly Skewed Count Data.
    (IAEME, 2019-01-11) Adesina, Olumide Sunday
    A big task often faced by practitioners is in deciding the appropriate model to adopt in fitting count datasets. This paper is aimed at investigating a suitable model for fitting highly skewed count datasets. Among other models, COM-Poisson regression model was proposed in this paper for fitting count data due to its varying normalizing constant. Some statistical models were investigated along with the proposed model; these include Poisson, Negative Binomial, Zero-Inflated, Zero-inflated Poisson and Quasi- Poisson models. A real life dataset relating to visits to Doctor within a given period was equally used to test the behavior of the underlying models. From the findings, it is recommended that COM-Poisson regression model should be adopted in fitting highly skewed count datasets irrespective of the type of dispersion.
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    Approximation techniques for maximizing likelihood function of generalized linear mixed models for binary response data
    (sciencepubco, 2018-12-30) Adesina, Olumide Sunday
    Evaluating Maximum likelihood estimates in Generalized Linear Mixed Models (GLMMs) has been a serious challenge due to some integral complexities encountered in maximizing its likelihood functions. It is computationally difficult to establish analytical solutions for the integrals. In view of this, approximation techniques would be needed. In this paper, various approximation techniques were exam-ined including Laplace approximation (LA), Penalized Quasi likelihood (PQL) and Adaptive Gauss-Hermite Quadrature (AGQ) tech-niques. The performances of these methods were evaluated through both simulated and real-life data in medicine. The simulation results showed that the Adaptive Gauss-Hermit Quadrature approach produced better estimates when compared with PQL and LA estimation techniques based on some model selection criteria.
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    Assessing the Impact of ATM and POS Transactions on Currency Circulation in Nigeria: a comparison of artificial neural network and linear regression models
    (Revue d'Intelligence Artificielle, 2024-02) Adesina, Olumide Sunday
    This work is aimed at assessing the impact of ATM and POS transactions on currency in circulation in Nigeria via the comparison of the Artificial Neural Network and Linear Regression models. The Pearson product moment correlation coefficient was used to assess the degree of relationship between currency in circulation and ATM transactions, currency in circulation and POS transactions, ATM transactions and POS transactions. In addition to this, a multiple linear regression model was also fitted on the data to in order to assess the impact of currency in circulation on ATM transactions and POS transactions. From the results obtained, it was observed that POS transactions have a significant impact on the amount of currency in circulation compared to ATM transactions because the value of POS transactions has a P-value of 1.5542E-13 which is significant at 0.05. This was further studied using the Artificial Neural Network (ANN). The Multilayer preceptor of ANN was adopted, with 70 percent of the data initially subjected to training and 30 percent for validity. Thereafter, the amount of training data was increased to 61.4% and testing data was increased to 38.6% and it was observed that there was corresponding increase in the R-square from 0.809 to 0.839. This shows that the R-square of the ANN can be improved by increasing the amount of training data and testing data.
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    Assessment of noise-levels of generator-sets in seven cities of South-Southern Nigeria,
    (Taylor & Francis, 2018-01-21) Adesina, Olumide Sunday
    Noise pollution has been shown to be a global health hazard and this could be aggravated by the use of noise-emitting generators. Therefore, this study aims to determine the Sound Pressure Levels (SPLs), Sound Power Levels (LW) as well as Noisiness of sixty different models with various Power-ratings from fourteen generator brands, commonly used in homes/offices in seven cities covering South-Southern Nigeria. The results obtained between January 2013– December 2015 showed that for nearly all generator brands, models and ratings, the values of SPL were above the Permissible Noise Exposure Limits (PNELs) recommended by WHO, USEPA and EN of 90dB(A), 75dB(A) and 70dB(A) respectively for 8hour daytime safe human exposure. Also, the ‘Wilcoxon Signed Ranks Test’ analyses showed that the three (3) alternative hypotheses [Ha : SPL . 90dBA], [Ha : SPL . 75dBA] and [Ha : SPL . 70dBA] are statistically-significant and should be accepted – further implying that: the decibel-ratings of majority of these generator-models are evidently hazardous.
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    Bayesian Dirichet Process Mixture Prior for Count Data
    (IAEME, 2018-12-13) Adesina, Olumide Sunday
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    Bayesian Multilevel Models for Count Data
    (Nigerian Society of Physical Sciences, 2021-08-29) Adesina, Olumide Sunday
    The traditional Poisson regression model for fitting count data is considered inadequate to fit over-or under-dispersed count data and new models have been developed to make up for such inadequacies inherent in the model. In this study, a Bayesian Multi-level model was proposed using the No-U-Turn Sampler (NUTS) sampler to sample from the posterior distribution. A simulation was carried out for both over-and under-dispersed data from discrete Weibull distribution. Pareto k diagnostics was implemented, and the result showed that under-dispersed and over-dispersed simulated data has all its k value to be less than 0.5, which indicates that all the observations are good. Also, all WAIC were the same as LOO-IC except for Poisson in the over-dispersed simulated data. Real-life data set from National Health Insurance Scheme (NHIS) was used for further analysis. Seven multi-level models were fitted and the Geometric model outperformed other models.
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    Bayesian Optimization for Parameter of Discrete Weibull Regression
    (SCIENCEDOMAIN International, 2020-03-13) Adesina, Olumide Sunday
    This study aim at optimizing the parameter θ of Discrete Weibull (DW) regression obtained by maximizing the likelihood function. Also to examine the strength of three acquisition functions used in solving auxiliary optimization problem. The choice of Discrete Weibull regression model among other models for fitting count data is due to its robustness in fitting count data. Count data of hypertensive patients visits to the doctor was obtained at Medicare Clinics Ota, Nigeria, and was used for the analysis. First, parameter θ and β were obtained using Metropolis Hasting Monte Carlo Markov Chain (MCMC) algorithm. Then Bayesian optimization was used to optimize the parameter the likelihood function of DW regression, given β to examine what θ would be, and making the likelihood function of DW the objective function. Upper confidence bound (UCB), Expectation of Improvement (EI), and probability of Improvement (PI) were used as acquisition functions. Results showed that fitting Bayesian DW regression to the data, there is significant relationship between the response variable, β and the covariate. On implementing Bayesian optimization to obtain parameter new parameter θ of discrete Weibull regression using the known β, the results showed promising applicability of the technique to the model, and found that EI fits the data better relative to PI and UCB in terms of accuracy and speed.
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    Bayesian Regression Model for Counts in Scholarship
    (IISTE, 2017-07-30) Adesina, Olumide Sunday
    Discrete Weibul (DW) is considered to have the ability to capture under and over-dispersion simultaneously and also have a closed-form analytical expression of the quantiles of the conditional distribution. There is a need to further investigate how effective the model is, as compared to other competing models in the context of classical and Bayesian technique. In this study, the strength of DW is investigated, for both on frequentist and Bayesian technique. The Bayesian DW adopts parameterization, which makes both parameters of the discrete Weibull distribution to be dependent on the predictors. Bayesian Generalized linear mixed model is also implemented and is compared with the BDW, since Bayesian generalized linear mixed model is known to be robust in handling over-dispersion in count data. A simulation study and real life data was carried out for over and under-dispersed count data. The empirical analysis shows the superiority of Bayesian Generalized linear mixed model over Bayesian DW in the case of over-dispersed data as identified in the simulated data and real life data, but not for under-dispersed data as in the case of simulated study.
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    Bayesian Spatial Analysis of Socio-Demographic Factors Influencing Smoking, Use of Hard Drugs and Its Residual Geographic Variation among Teenagers of Reproductive Age in Nigeria
    (iieta, 2022-02-01) Adesina, Olumide Sunday
    The use of hard drugs (Alcohol, cocaine, and Nicotine) has remained the censorious issue globally and in Nigeria. The use of hard drugs and tobacco smoking is common in the stage of adolescence and youth life, which is a deterrent to education and career advancement. Hence, this study looks into socio-demographic factors that influence the use of hard drugs and tobacco smoking among teenagers between the ages of 15 years to 19 years. To achieve this objective, cross-sectional data was used and secondary data was obtained from DHS - National Demographic and Health Surveys (NDHS) from the survey year 2018. Some Bayesian models were developed and Conditional Autoregressive (CAR) model with random walk 1 (RW1) was the best model. The study unveiled a positive significant association of settlement, previous place of residence, education attainment, religion, ethnicity, literacy with reported use of hard drugs amongst teenagers of reproductive age.
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    Count Models Analysis of Factors Associated with Road Accidents in Nigeria
    (IIETA, 2022-08-19) Adesina, Olumide Sunday
    The current state of all Nigerian roads is in poor condition, and reports of accidents have been recorded across the federation. The larger mission of the sustainable development goal is to promote sustainable cities and communities. This research study aims to examine factors responsible for road accidents in Nigeria through the quantitative tool of higher extensions of the Poisson regression model (ZTNPRM). A cross-sectional study design was adopted and secondary data was used within a sample period from the 1st quarter of 2006 to the 2nd quarter of 2020. Due to overdispersion, ZTNPRM indicates human errors contribute to a large proportion (41.4%) of road accidents. Vehicle factors are also statistically and positively related to road accidents. All the factors this model identified that lead to road accidents predicted low road accidents. Hence, the study recommends that Nigerian car users follow all rules and regulations associated with safe driving and make the environment safer for people as the sustainable development goal (SDGs). This study recommends more attention to the area of accident and injury prevention as a strategic objective of the SDGs.
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    COVID-19: Dynamics of Socio-Economic Shocks on Energy Sector Performance in Africa
    (Emerald Publishing Limited, 2022-01-01) Adesina, Olumide Sunday
    The resistance of the energy sector in recent time has been tried by COVID-19 as the occurrence has added to the dampening down demand for crude oil which has resulted in volatility in prices and dwindling production of crude oil at the global crude oil market. Dwindling demand and price decline can also be perceived as a trend in the electricity sector, the electricity price (ELECTP) and consumption. The consequences of individual policy response by countries in relation to the socio-economic impact of COVID-19 is yet to be known, and recent studies conducted in the continent are yet to document the impact of the pandemic on the oil-producing African countries. It is in light of this that this study determined the effect of socio-economic shocks activated through the COVID-19 pandemic on the energy sector performance and economic development of Africa. Monthly data were sourced from the United States Energy Information Administration, COVID-19 geographic distribution worldwide, World Development Indicator and Trading Economics from 2019 (m12) to 2020 (m10). Seventeen oil-producing African countries were selected across the region based on data availability. The structural panel vector auto regression (SPVAR) analytical technique was used in estimating COVID-19 pandemic and socio-economic shocks on the energy sector performance proxied by oil production (OILP), ELECTP and economic development proxied by gross domestic product per capita (GDPPC) of the countries. Findings revealed that the COVID-19 pandemic transmits a negative shock to OILP and GDPPC while a positive shock is transmitted to ELECTP. The socio-economic variables also transmitted both the positive and negative shocks to OILP, ELECTP and GDPPC. Therefore, the study recommended that policies should be directed towards putting in place a shock-absorbing mechanism so as to cushion the effect of the identified shocks on the performance of the energy sector and the economic development of the countries
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    Detection Of K-Upper Outliers In Exponential Samples Using Multiple Upper Outlier Tests
    (Annals. Computer Science Series, 2019-05-21) Adesina, Olumide Sunday
    Outlying values have been an issue of concern to researchers and data analysts. The study of multiple outliers is imperative because outlying values may lead to model misspecification, wrong estimation of parameters and incorrect statistical results. This paper proposed Tietjien-Moore test statistics for upper outliers in exponential samples. A simulated study was carried to investigate the strength of the test statistics.
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    E-payment Challenges: The Genesis and Remedies to the Problem
    (SCIENCEDOMAIN : Journal of Scientific Research & Reports, 2022-05-16) Adesina, Olumide Sunday
    Switching between internet solutions in an organization can be challenging, mainly if it affects the core operations and running of the organizations, such as e-payment. It becomes problematic when the role of e-government is not properly considered in an organization. In this study, the school fees e-payment project of a Nigerian University and the losses incurred due to the lack of incorporation of the ICT- government into the e-payment process was evaluated. This study recommends good outsourcing, corporate, and ICT governance practices such as the incorporation of frameworks like the Control Objectives for Information and Related Technology (COBIT) and IT Infrastructure Library (ITIL), which can be integrated to minimize such problems. This would help any University to avert likely losses.
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    An Empirical Comparison of Academic Performance of Distance Learning Students and Traditional Students
    (University of Assumption, 2018-04-30) Adesina, Olumide Sunday
    The importance of distance learning programmes in tertiary institutions around the globe, cannot be over-emphasized, as it provides an alternative mode of obtaining a university degree, through the use of information technology. Currently, the world is operating in a technology and social media dominated era where millions of citizens can access limitless information. This study investigates the academic performance of graduates from traditional, and distance learning, modes of education in accounting and business administration courses, with the goal of determining the existence of differences in academic performance. The measure of academic performance considered in this study is the graduating Cumulative Grade Point Average (CGPA) of students. The results suggest that although marginal differences exist between the categories of students in performance, these differences are not significant enough to suggest difference in a performance due to the study mode, thus, this study concludes that performance of students in the selected courses is similar irrespective of the mode of education.
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    Exploring Robust Methods for Testing Equality of Variances
    (International Journal of Statistics and Economics, 2018) Adesina, Olumide Sunday