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Author(s): Durowade Adeyemi Nathaniel, Ajayi Gbenga Johnson, Ayobami Samuel O..

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

Emotional abuse refers to a pattern of behaviour where one person seeks to control, manipulate, and dominate another person, often causing emotional harm and trauma. It is committed more frequently against women. The repeated occurrence of this in Kenya necessitated the conduct of this research work on the factors associated with emotional violence in the country. A total of 8444 respondents were considered in this study. We employed binary logistic, probit, and complementary-log log regression on the retrieved data. The data were collected from the Kenya Demographic Health Survey (KDHS) via their website. Emotional violence has been identified as one of the most prevalent form of violence against women globally (WHO, 2013). On average, Intimate Partner Violence (IPV) affects about 47% of the Kenya population. This study aimed to model some risk factors influencing emotional violence in Kenya and the investigation was carried out by observing the extent to which selected covariates such as number of other wives, number of children under the age of five , age at first marriage, partner’s age, education status, husband living in the house, husband’s smoking habit, marital duration, spending decision, number of sexual partner(s), husband’s jealousy, accusation from husband, money trust and partner’s ten-year age difference influence emotional violence. The result of the research work ascertained that “accusation from husband” is the factor which has the most significant impact on emotional violence.

Keywords:

Binary logistic regression; Probit regression; C-log log; Emotional violence; ROC curves; Goodness of fit.

No. of Downloads: 0

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Author(s): A. J. Adjekukor, C. O. Aronu.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

This study evaluates the robustness of Ordinary Least Squares (OLS) and Double Weighted M-Estimation (DWME) methods for predicting crude oil prices in Nigeria, focusing on predictive accuracy and generalization. Using 192 monthly data points (2006–2021) from the Central Bank of Nigeria (CBN) and Nigerian National Petroleum Corporation (NNPC), the dataset included crude oil prices, production, crude oil production, and exchange rates, with synthetic datasets simulated via multivariate normal distribution for varying dimensions (n = 10 to 1,000). The performance measures such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared were assessed. Results showed comparable MSE values for training data, with OLS_TRAIN ranging from 172.85 to 694.56 and DWME_TRAIN from 173.03 to 699.27. Testing data revealed DWME's marginal superiority, with slightly lower MSE (e.g., DWME_TEST median 548.68 vs. OLS_TEST median 543.85). MAE trends indicated consistency for both methods, with DWME showing marginally better stability across dimensions. R-squared values highlighted improved generalization for smaller datasets, with DWME_TEST peaking at 0.7043 and OLS_TEST at 0.7544 for the 10x3 dimension. Both methods struggled with generalization as dimensions increased but exhibited stable training performance. In conclusion, DWME demonstrated slightly better robustness, especially in testing scenarios, affirming its suitability for predictive tasks involving economic and energy-related variables.

Keywords:

Mean squared error, Mean absolute error, R-squared, Multivariate normal distribution, Crude oil production, Exchange rate.

No. of Downloads: 0

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Author(s): Shammah Sunday Kpanja, Madaki Atama Alhamdu.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

This paper focused on the analysis of a production inventory where the production rate and holding cost are linearly dependent, while the demand is stock dependent demand rate. The production inventory model is formulated using system of differential equations and integral calculus including initial boundary/matching conditions and integral calculus were also used to analyse the inventory problem. These differential equations were solved to give the best cycle length that will minimize the inventory cost per unit time. A Mathematical theorem and all its proof is presented to established the convexity of the cost function. A numerical example is also given to demonstrate the applicability of the model developed accompanied by sensitivity analysis to see the effects of the parameter changes.

Keywords:

Linear Holding cost, Production, Inventory, Demand, Stock dependent, Rate.

No. of Downloads: 0

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Author(s): Idochi Okenwe, Isaac Didi Essi, Anthony Ike Wegbom.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

This study examined the comparison between Cox and Weibull survival models in estimating the determinants of divorce in Rivers State, Nigeria. Data consisting of Demographic, Socio-economic and Treatment related variables were collected from Judiciary High court for a period of 10 years for the analysis. The Factors estimated were; Age at Marriage of Husband and Wife, Presence of Children, Duration of Marriage, Employment Status of Husband and Wife, Educational Level of Husband and Wife, Number of Counselling Sessions and Court Sittings attended. Cox proportional Hazard (Semi-parametric) and Weibull (Parametric) regression models were compared for a better fit in estimating the determinants of the risk of divorce among couples, using the Akaike Information Criterion (AIC). The result showed that Cox proportional hazards regression model performed better than Weibull regression model with a difference of 44.5 AIC value lower than that of the Weibull model. Hence, Cox PH model revealed that, of all the factors estimated; Employment Status of husband, Presence of children and Duration of marriage had significant effect on the risk of divorce. Specifically, Employment status of husband and Duration of marriage reduced the risk of statutory marriage divorce by 3% and 41% respectfully, while Presence of children in statutory marriage increased the risk of divorce by 72%. The study thereby recommended among others that the husband, who is the head of the family, should strive, struggle, engage and explore legitimate and genuine jobs or businesses to be able to provide the immediate needs of his family, because the marriage of a jobless and idle husband is always at the risk of divorce.

Keywords:

Cox model, Weibull model, Divorce, Statutory marriage, Survival analysis, Nigeria.

No. of Downloads: 0

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Author(s): I. O. Akalagboro, C. O. Aronu, L. S. Mark.

Volume/Issue: Volume 8 , Issue 1 (2025)

Abstract:

This study presents a comparative analysis of six Fréchet distribution variants: Kumaraswamy Fréchet (KF), Exponentiated Fréchet (EF), Beta Fréchet (BF), Gamma Extended Fréchet (GExF), Odd Lomax Fréchet (OLxF), and the standard Fréchet (F) focusing on their structural properties, parameter estimation, and model performance. These distributions, characterized by varying levels of complexity and flexibility, are particularly effective for modelling extreme values and heavy tails, crucial in fields like econometrics and reliability analysis. Differences in Probability Density Functions (PDFs) reveal the enhanced adaptability of BF and GExF variants, attributed to their additional beta and gamma components. The models were applied to three datasets: Jobs made of Iron Sheets, Airborne Communication Transceiver Repairs, and Tax Revenue. The performance of the distributions under study was evaluated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The finding showed that the standard Fréchet distribution consistently outperformed its variants, achieving the lowest AIC and BIC values across datasets, indicating a superior balance of simplicity and adaptability. EF and KF variants demonstrated competitive performance but lacked the robustness of the standard Fréchet model, while OLxF and GExF showed higher AIC and BIC values due to potential over-parameterization. This study underscores the importance of aligning model complexity with dataset characteristics and highlights the standard Fréchet distribution as a versatile choice for analyzing extreme data.

Keywords:

Fréchet distribution, Parameter estimation, Model performance, Extreme values, Heavy tails, Over-parameterization.

No. of Downloads: 0