Browsing by Author "Adesina, Olumide S."

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Regularized Models for Fitting Zero-Inflated and Zero-Truncated Count Data: A Comparative Analysis
(2023) Akinlabi, Grace O.; Adesina, Olumide S.; Okewole, Dorcas M.; Adedotun, Adedayo F.; Adekeye, Kayode S.; Edeki, Onos S.
Generalized Linear Models (GLMs) are widely recognized for their efficacy in fitting count data, superior to the Ordinary Least Squares (OLS) approach. The incapability of OLS to suitably handle count data can be attributed to its tendency to overfit. This study proposes the utilization of regularized models, specifically Ridge Regression and the Least Absolute Shrinkage and Selection Operator (LASSO), for fitting count data. These models are compared to frequentist and Bayesian models commonly used for count data fitting, such as the Dirichlet prior mixture of generalized linear mixed models and the discrete Weibull. The findings reveal Ridge Regression's superiority over all other models based on the Akaike Information Criterion (AIC). However, its performance diminishes when evaluated using the Bayesian Information Criterion (BIC), even though it still outperforms LASSO. The study thereby suggests the use of regularized regression models for fitting zero-inflated count data, as demonstrated with simulated data. Further, the appropriateness of regularized zero for zero-truncated count is exemplified using life data.
Statistical Learning Insights on Nigerian Aviation Service Quality
(International Journal of Transport Development and Integration, Volume: 8, Issue Number: 1, 2024) Adesina, Olumide S.; Adedotun, Adedayo F; Ayoola, Femi J; Adesina, Tolulope F; Alayande, Semiu A.; Onayemi, Oluwakemi O
This investigation employs statistical learning techniques to analyze service quality within Nigeria's aviation industry, a sector integral to the nation's economic vitality and connectivity. The industry has faced challenges exacerbated by economic downturns, notably the rise in fuel prices and the devaluation of the Nigerian Naira since early 2022. Previously reported customer dissatisfaction prompted a thorough examination of passenger and stakeholder experiences. A cross-sectional survey methodology was adopted, yielding data subsequently analyzed through advanced machine learning algorithms. A principal component analysis (PCA) model was refined via leave-one-out cross-validation (LOOCV), an unsupervised learning approach. Findings reveal that crew member performance is the most influential factor on service quality, exhibiting an inverse relationship with other variables in the first principal component. In the second principal component, flight rescheduling emerges as a significant negative determinant. Recommendations from this analysis are directed at aviation industry practitioners, policymakers, and stakeholders, emphasizing the enhancement of crew member recruitment and training processes. Additionally, strategies to adhere to scheduled travel times are advocated. These insights are pivotal for advancing service standards in Nigeria's airline industry.
Unsupervised learning analysis of European working condition
(Cogent Business & Management VOL. 11, NO. 1,, 2024) Adesina, Olumide S.; Adedotun, Adedayo F.; Alayande, Semiu A.; Efe-Imafidon, Emmanuel O.; Adesina, Tolulope F.; Okagbue, Hillary I.; Onayemi, Oluwakemi O.
Workers require good working conditions to enhance their job performance, in this study, we conducted a survey of European working conditions in 2022 and compared the results with that of 2016 using an unsupervised learning approach for exploratory data analysis and determining the relationships. Hence, the Principal Component Analysis (PCA ) was adopted. The analyses were in two parts for both the 2016 and 2022 surveys. Following the PCA , the first part shows that European workers are mostly characterized by cheerfulness and good spirits. The second part reveals that European workers are best characterized by enthusiasm in their work. Test statistics showed that the European working condition for the two periods does not differ significantly. The working conditions in Europe have not been altered in the space of six years. This study recommends that the working condition in Europe should be improved so that employers would continue to give their best.