College of Science and Technology
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Item 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.Item Three attributes determining land values in three selected housing estates in Uyo, Nigeria(Frontiers in Sustainable Cities, 2024) Iroham, Chukwuemeka O.; Okagbue, Hilary I.; Ekanem, Inimfon F.; Peter, N. J.; Samuel, Olugbemisola W.; Nto, Sunday E.; Isiaka, Saheed; Adedotun, Adedayo F.There is a knowledge gap regarding the specific attributes (location-specific, environmental, and neighborhood) that impact land value, the relationship among these attributes, and the degree of impact on the land values in residential estates in Uyo, Nigeria. The three factors all combine to create a unique picture of a place, impacting its desirability and ultimately, its land value. This study explores the relationship between various land value attributes within specific residential estates (Ewet Housing Estate, Shelter Afrique Estate, and Akwa-Ima Estate) in Uyo. A questionnaire was designed and used to solicit data from the respondents living in the three estates with the aid of the purposive sampling technique. The findings revealed the following: Closeness to school is the location-specific attribute that contributes the most to land value, and closeness to recreational centers contributes the least. The presence of security and police stations had the most significant contribution to land value, and the presence of noise in the neighborhood had the least significant contribution. Peace, quiet, and beauty had the most significant contribution to land value, and the presence of lakes and water bodies in the environment contributed the least to land value. The factor analysis yielded two major factors for location-specific attributes: ‘transport’ and ‘place’. The factor analysis grouped the neighborhood attributes into two factors: ‘easily controlled’ and ‘not easily controlled’. All three attributes of land values are positively correlated with one another. Implications for research and recommendations were made.