Statistics http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/70 2026-05-02T06:58:24Z 2026-05-02T06:58:24Z Ibrahim, A. Adeyemi, R.A. Usman, A. Musa, A.O. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29630 2025-05-14T16:15:32Z 2024-10-01T00:00:00Z

Title: Bayesian Geo-Additive Modelling of State-Level Heterogeneity in Anaemia Prevalence and Risk Factors among Under-Five Children in Nigeria Authors: Ibrahim, A.; Adeyemi, R.A.; Usman, A.; Musa, A.O. Abstract: Anemia is a very serious global health issue, particularly in sub-Saharan African countries, where it has significant implications for both human health and economic development. This study aims to identify the socioeconomic, demographic and climatic factors as well as geographical variations linked to the prevalence of anaemia in young children in Nigeria, based on the results of the 2018 Nigeria demographic and health survey. The association between various types of covariates and possible spatial variations was explored using a hierarchical Bayesian geo-additive modelling approach. In particular, the study focused on a binary response variable. Out of the four formulated semi-parametric models, geo-additive model with both structured and unstructured random effects was found to be the best fitted. The findings of the study reveal significant spatial variation in anemia risk, with the highest risk observed in the states of Sokoto, Niger, Akwa Ibom, Ebonyi and Bauchi. Besides, educational level of a child’s mother, wealth status of household, a child’s area of residence, prevalence of malaria and land surface temperature were all associated with childhood anemia. The prevalence of infant anemia decreased with increasing child's age. Mother’s Body Mass Index also has inverse relationship with the risk of childhood anaemia. Given the observed state-level patterns of anemia risk, it is important to implement targeted programs that address the specific needs of vulnerable children in each state. Th is could help reduce the prevalence of childhood anemia in the Nigeria. Description: Anemia is a very serious global health issue, particularly in sub-Saharan African countries, where it has significant implications for both human health and economic development.

2024-10-01T00:00:00Z Ibrahim, Aminu Adeyemi, R.A. Usman, Abubakar Adabara, Nasiru U. http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29628 2025-05-14T16:05:07Z 2025-08-01T00:00:00Z

Title: Weighted Random Effects Multinomial Model with Application to Anemia and Malnutrition Comorbidity among Under five Children in Nigeria Authors: Ibrahim, Aminu; Adeyemi, R.A.; Usman, Abubakar; Adabara, Nasiru U. Abstract: This study develops multinomial models with weighted random effects to analyze the spatial pattern and risk factors associated with anemia, malnutrition, and their co-occurrence among children under the age of five in Nigeria. A Bayesian hierarchical multinomial model with weighted random effects and adjusted Intrinsic Conditional Auto regressive (ICAR) prior for the random effects, was used to account for the comorbid patterns of anemia and malnutrition among young children in Nigeria. The study utilized data from the 2018 Demographic and Health Survey. The structured random effects were weighted to reflect state-level variation in precipitation, a climatic factor considered to influence child health outcomes. The results of fixed effects indicated that area of residence, maternal education level, and household wealth status were significant predictors of anemia and malnutrition co-occurrence. The generated map identified the north eastern region of the country with low average precipitation as a high-risk region for anemia and malnutrition co-morbidity. These findings emphasize the need for targeted interventions to mitigate precipitation-related health risks and public health campaigns focusing on maternal education on child nutrition, hygiene, and disease prevention. Description: Anemia, malnutrition, and their co-occurrence among children under the age of five in Nigeria. A Bayesian hierarchical multinomial model with weighted random effects

2025-08-01T00:00:00Z THANKGOD, JOSHUA AUDU, ISAH ABDU;MUDALLIB, IBRAHIM ABDULLAHI, USMAN MOHAMMED LAWAN, DANYARO http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29524 2025-05-12T14:10:13Z 2023-10-05T00:00:00Z

Title: COMPARATIVE ANALYSIS OF GEOMETRIC BROWNIAN MOTION, ARTIFICIAL NEURAL NETWORK AND NAIVE BAYESIAN TECHNIQUES USING NIGERIA STOCK EXCHANGE DATA Authors: THANKGOD, JOSHUA; AUDU, ISAH; ABDU;MUDALLIB, IBRAHIM; ABDULLAHI, USMAN; MOHAMMED LAWAN, DANYARO Abstract: This paper presents a comparative analysis of three market price forecast models, namely Geometric Brownian Motion, Artificial Neural Network, and Naive Bayesian techniques, using data from the Nigeria Stock Exchange. The exploratory data analysis results indicate slight variations in the mean and median of the log stock price over a 5-year period. The data shows a relatively small spread from the mean and approximately symmetric distribution, as indicated by a skewness value close to zero. The normality test confirms that the log stock price data follows a normal distribution. The forecast using Artificial Neural Network (ANN) shows a minimal change in future stock price, suggesting low returns and moderate risk in the Nigerian stock market. The graphical representation of the ANN model demonstrates a constant path with little variation. Similarly, the Naive Bayesian technique provides a similar forecast to the ANN model, indicating limited profit potential. The Geometric Brownian Motion model also forecasts little variation in future stock prices, with 2023 showing slightly higher values. The accuracy of the forecast models is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the ANN model has an error of 4.60%, the Naive Bayesian model has an error of 9.29%, and the Geometric Brownian Motion model has an error of 12.67%. These findings suggest that the ANN model performs better in terms of accuracy compared to the other two models.

2023-10-05T00:00:00Z ABDULLAHI, USMAN ISAH, AUDU ADEYEMI, RASHID OLAYEMI, KAYODE http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/29520 2025-05-12T13:35:47Z 2023-04-03T00:00:00Z

Title: Covariate Effect in Spatio-temporal Bayesian Model with Two-level Spatial Structure Authors: ABDULLAHI, USMAN; ISAH, AUDU; ADEYEMI, RASHID; OLAYEMI, KAYODE Abstract: Spatio-temporal models suffer from comparability problems of relative risks (RRs) based on the removal of the covariate effect as a confounding factor on the risk esti- mate of the study population through distribution of standardized mortality ratios (SMRs). Two spatio-temporal models with two-level spatial structure with different approaches are considered in this study for comparison. The first model followed SMRs procedure by re- moval of the effect of the confounding factors on the risk estimate in the study population through distribution standardization., while the second model included covariate effect as confounding factors on the risk estimate in the study population. The two models were fitted within a hierarchical Bayesian framework with integrated nested Laplace approxi- mation (INLA) estimation procedures. The objectives of this study are to compare both models in terms of their performance and identify the age-group(s) of women with sig- nificant higher risk due to breast cancer disease. The models are applied to female breast cancer mortality data in Nigeria.

2023-04-03T00:00:00Z