Journal of Statistical Modeling and Analytics (JOSMA)
Journal of Statistical Modeling and Analytics (JOSMA) (ISSN: 2180-3102) is a biannually (April and November) peer-reviewed journal published by the Institute of Statistics Malaysia (ISMy) and Centre for Foundation Studies in Science, Universiti Malaya. It provides a platform that presents manuscripts devoted to all types of research in Statistical Modeling and Analytics fields. JOSMA is currently undergoing a substantial relaunch and we do look forward contributions from members as well as academicians world wide. Indexing JOSMA is indexed by MyJurnal and Google Scholar.
Malaysia Institute of Statistics (ISMy) and Centre for Foundation Studies in Science, Universiti Malaya,
Autoregressive Modelling with Seasonal Variations for Malaysian Crude Palm Oil Price Forecasting
Crude palm oil (CPO) plays a vital role in Malaysia’s economy, yet its price dynamics remain highly volatile due to global market fluctuations, policy changes, and demand uncertainties. Reliable short-term forecasting is therefore essential for industry stakeholders and policymakers. This study employs Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) modelling to analyze and forecast monthly Malaysian CPO prices from 2015 to 2024. Preliminary seasonality diagnostics using STL decomposition indicated weak and statistically insignificant seasonal patterns, with a seasonal strength of 0.2046. Consequently, seasonal differencing was unnecessary, and first-order non-seasonal differencing was sufficient to achieve stationarity. Model identification and estimation were performed using an in-sample dataset (2015–2022), while model validation was conducted using out-of-sample data (2023–2024). A manual grid search across ARIMA candidates identified ARIMA(3,1,3) as the optimal model based on the Corrected Akaike Information Criterion (AICc). Residual diagnostics confirmed that the model errors behaved as white noise with no remaining autocorrelation. Out-of-sample testing further demonstrated that ARIMA(3,1,3) produced satisfactory predictive accuracy across RMSE, MAE, and MAPE metrics. Overall, the findings indicate that non-seasonal ARIMA models are sufficient for CPO price forecasting and that ARIMA(3,1,3) provides a reliable framework for short-term prediction and decision-making.
Copyright (c) 2025 Journal of Statistical Modeling and Analytics (JOSMA)
Comparative Analysis of Machine Learning Models for Vintage-Based Credit Scoring
Accurate credit risk assessment is crucial for financial institutions to minimise loan defaults. This study proposes a vintage-based credit scoring framework that integrates individual repayment behaviour with vintage analysis and evaluates five machine learning models, including logistic regression, random forest, XGBoost, stacking ensemble, and multilayer perceptron (MLP), for binary credit risk classification. Results show that ensemble methods, particularly random forest, achieve superior predictive performance with the highest F1-score (0.81), precision (0.87) and accuracy (0.96), while logistic regression exhibits high recall but low precision. The MLP shows good recall (0.79) and a competitive F1-score (0.77), making it suitable for prioritising high-risk borrower detection, although it lacks interpretability. Overall, the study highlights the trade-offs between predictive performance and interpretability, emphasising the potential of vintage-based approaches and ensemble learning for practical credit scoring applications.
Sample Size Requirements for the Central Limit Theorem for Skewed Distributions: A Simulation Study
The Central Limit Theorem (CLT) plays a foundational role in statistical inference, often serving as the rationale for assuming a normal approximation of the sample mean. Yet, the pace at which this assumption becomes valid is influenced by the shape of the parent distribution, especially its skewness. This research quantifies the minimum number of observations required for the mean of samples drawn from skewed, non-normal distributions specifically Gamma, Poisson, Binomial, and Beta to achieve a satisfactory normal approximation. We implemented a Monte Carlo simulation and applied both the Shapiro-Wilk and Kolmogorov-Smirnov tests to assess the adequacy of the normal approximation. Results indicate a nonlinear association between the degree of skewness and the sample size required for acceptable normal approximation. For distributions with mild asymmetry (|skewness| < 0.5), 20 samples often suffice, whereas more heavily skewed distributions (|skewness| ≥ 2.5) may necessitate sample sizes beyond 100. These findings call into question the blanket use of the "n ≥ 30" heuristic and suggest more tailored guidelines are necessary for accurate inference. A graphical overview summarizes these results across the examined distributional families, offering clear guidance for applied researchers working with non-normal data.
Modeling birth registration in the Savannah Region of Ghana
Accurate birth registration plays a vital role in national development planning and organizational decision-making. The Births and Deaths Registry is a key provider of demographic information, offering insights into the composition, magnitude, growth, and spatial distribution of a country's population across different administrative divisions. Inaccurate birth records can lead to misallocation of resources, such as immunization supplies, educational funding, and child protection initiatives. This research examines the factors influencing birth registration in the Savannah Region of Ghana, based on secondary data obtained from the regional Births and Deaths office for the years 2020 and 2021. The analysis was conducted using a Linear Mixed Effects model. The results indicate that parental education levels, employment status, the identity of the informant, and the location of residence significantly impact birth registration outcomes. The findings highlight an urgent need to improve public awareness, especially within rural areas and communities with low literacy levels, in order to increase birth registration rates.
Copyright (c) 2026 Journal of Statistical Modeling and Analytics (JOSMA)
Modeling Colon Cancer Survival using a Proportional Hazard Mixture Cure Model with Principal Component Covariates
This study explores how gene expression data can help predict the survival times of colon cancer patients. Since the dataset is high-dimensional, Principal Component Analysis (PCA) reduces complexity while retaining essential information. Based on eigenvalue one criteria, proportion of variance accounted for, and scree plot analysis, 60 principal components (PCs) are selected as covariates. These are then used in a Proportional Hazard Mixture Cure Model, applying both Cox and Weibull as baseline models to differentiate between cured and uncured patients over a five-year follow-up period. Maximum Likelihood Estimation (MLE) is applied to estimate the model parameters. The results show that the Cox model provides more reliable estimates, indicated by lower AIC values, higher hazard rates, and statistically significant p-values (<0.05). On the other hand, the Weibull model finds no significant covariates (p-values >0.05), with only the intercept being significant. Furthermore, the Weibull model estimates a 100% cure rate, while the Cox model estimates 56%, suggesting that the Cox model provides a better fit for predicting survival outcomes. By integrating gene expression data into survival modeling, this study offers a more accurate and interpretable way to understand patient outcomes. The findings highlight the Cox mixture cure model as a valuable tool for guiding clinical decisions.
Copyright (c) 2025 Journal of Statistical Modeling & Analytics (JOSMA)
Performance of Chili Price Forecasting Models in Johor: A Comparative Study
A substantial portion of household income in Malaysia is allocated to food expenditures, and chili is a staple ingredient in Malaysian cuisine. Fluctuations in chili prices directly affect the cost of living for individuals and families, impacting their purchasing power and overall well-being. Forecasting chili prices helps in effective supply chain management. Producers, distributors, and retailers can plan and adjust their operations based on anticipated price trends. This, in turn, contributes to the country's efficiency and stability of the chili supply chain. This study emphasises the importance of comparing various forecasting models to identify the most accurate predictors of chili prices. The goal is to develop a model that can contribute to more informed decision-making in crop production and market interventions, ultimately promoting stability in the chili industry and ensuring sustainable practices. Statistical models, time series forecasting models and machine learning models which include multiple linear regression (MLR), Auto Regressive Integrated Moving Average with exogenous inputs (ARIMAX), and machine learning models that consist of Support Vector Regression (SVR) were tested and compared using ex-farm prices in Johor with the duration of 5 years, starting from 2018 to 2022. This study reveals that SVR under machine learning algorithms performed best as the forecasted model followed by ARIMAX and MLR. However, ARIMAX models, an extension of the ARIMA model, effectively capture and predict patterns by incorporating significant exogenous variables. Overall, the results show that the price of fertilisers, Movement Control Order (PKP) season and chili production significantly affect the prices of chilies.
Evaluating the Impact of Drought on Palm Oil Productions in Malaysia: Insights for Agricultural Modernization
This study investigates the impact of drought on palm oil production in Malaysia, focusing on Melaka. Using the six-month Standardized Precipitation-Evapotranspiration Index (SPEI-6), it quantifies drought severity and its influence on yields. Malaysia, as a major palm oil producer, faces challenges from climate variability, particularly during dry periods. Statistical analysis reveals a correlation coefficient (R) of 0.725, demonstrating a strong positive relationship between SPEI-6 values and palm oil yields, where wetter conditions correspond to higher yields. The study evaluates the effects of six-month drought intervals, detailing the statistical models and variables analyzed. These findings highlight patterns that can enhance agricultural resilience and resource management in the palm oil sector, particularly in Melaka. By addressing climate-related vulnerabilities, this research provides actionable insights for mitigating climate impacts and strengthening adaptive strategies within Malaysia’s key agricultural sectors.
Modelling The Prevalence of Some Infectious Diseases Among Farmers in Benue using Poisson Autoregressive Model
The aim of this work is to model the infection rates of some infectious diseases among farmers in Benue state using Poisson autoregressive model. The study utilizes monthly secondary data on serologically confirmed infection cases of Human Immunodeficiency Virus (HIV), tuberculosis (TB), and viral hepatitis (VHP)—the data span from January 2010 to December 2022. The study employs summary statistics and the Anderson-Darling normality test, time plots, bar graphs, and the Poisson autoregressive model as the principal methods of investigation. Results show that HIV, TB, and VHP have positive and increasing trends over time with non-Gaussian tendencies. All three infections peaked between 2017 and 2019 and had their lowest occurrence in 2010, indicating a potential common relationship among them. The results of the PAR (1) model indicate positive time trends in the HIV, TB, and VHP infection rates, suggesting that infections are increasing over time. The results further revealed a significant monthly increase of 2.25% in HIV, 1.22% in TB, and 12.08% in VHP respectively over the study period. The coefficient of determinations of the models explained 83.3%, 78.8%, and 80.8% of the variability in the HIV, TB, and VHP data indicating better fit for all the fitted models. The positive time trend suggests that monitoring the infection rates over time is crucial. Public health strategies need to account for this increasing trend and aim to reverse it.
Bayesian Structural Time Series Model and SARIMA Model for Rainfall Forecasting in Nigeria
Nigeria is recognized as being susceptible to climate change, and global warming if not taken care of, will lead to serious problems on livelihoods in Nigeria, especially in the area of agricultural activities. Rainfall is a major determinant of climate change the world over and climate change is one of the foremost global challenge facing humans at the moment. Using monthly time series rainfall data, Bayesian structural time series (BSTS) methodology was applied to fit models through MCMC algorithm. Also, Seasonal Autoregressive Moving Average (SARIMA) models were fitted to the same dataset using Box-Jenkins approach. The two models are considered based on their respective capacities to capture trend, seasonal and structural components of rainfall data. On the basis of model evaluation criteria (RMSE, MAE, MAPE and MASE), the SARIMA model had values that were clearly significantly smaller than that of the BSTS time series model. This implies that the SARIMA model is more robust in its estimations and forecasting abilities. Similarly, the R squared was larger for the SARIMA model than the BSTS (MCMC) model indicating that the SARIMA model was a better fit for the rainfall data. This study shows that SARIMA model is a more precise and robust in dealing with this type of dataset than BSTS (MCMC) model. It is better because its computational process using differencing, lags and moving averages ensure that the underlying components of the model are properly identified and estimated.
Developing a New Feature for Vulnerability Risk Scoring Model for Enhanced Cybersecurity
As organisations increasingly rely on technology, the risk of cyber threats to data integrity and security grows significantly. Traditional vulnerability risk-scoring models may not adequately address the rapidly evolving nature of cyber threats, necessitating the development of more adaptable and context-specific models. This research aims to achieve two primary objectives: developing a flexible risk-scoring model that can be customised for different industries, companies, or situations, and creating a new feature that accurately reflects the risk score based on the current dataset. The study employs correlation analysis and machine learning-based regression modelling, utilising appropriate evaluation metrics to assess model performance. Results indicate that the K-Nearest Neighbors regression model performs particularly well, offering precise risk assessments. The risk score produced by the model serves as a critical tool for prioritising cybersecurity efforts, where higher scores denote a greater need for immediate action. This research contributes to the field by providing a scalable and customisable framework for developing specialised risk-scoring models, enhancing the effectiveness of cybersecurity strategies across diverse contexts.
Copyright (c) 2024 Journal of Statistical Modeling & Analytics (JOSMA)
Malaysia’s Development Expenditure Effects on Gross Domestic Product by Using VECM Approach
The provision of public goods and services to citizens is a significant responsibility of the government. The products include schools, hospitals, roads, and other infrastructure. This investment is essential to stimulate economic growth, create job opportunities, and improve living standards. The effect of development spending on economic growth has been shown in a significant amount of existing literature. However, there are still various opinions on the impact level of development spending on economic growth. Therefore, the goal of our research is to investigate the link between Malaysia's Gross Domestic Product (GDP) and Development Expenditure (DE) based on the long-run and short-run vector error correction model (VECM) approach. The findings show that the long-term impact of GDP on development spending is positive, according to the results of the Johansen co-integration test. The long-run VECM also shows a positive correlation between GDP and government spending on development. Development spending and lag one GDP are negatively correlated. The short-run VECM shows a positive correlation between GDP and GDP lag. Unrestricted Vector Autoregressive (VAR) demonstrates that government spending on development has no discernible impact on GDP. According to the impulse response function (IRF) study, a GDP shock first has a negative effect on development spending before having a positive reaction. Although GDP might not be a strong indicator of DE in the near run, it becomes increasingly apparent over longer time frames, emphasizing the intricate relationship between macroeconomic factors and fiscal policy.
Enhancing Diabetes Mellitus Onset Prediction through Advanced Ensemble Learning Techniques
Type 2 diabetes is a major worldwide health issue, necessitating accurate and effective prediction models for timely intervention. Traditional machine learning (ML) models often underperform with imbalanced datasets and complex data relationships, resulting in suboptimal predictive accuracy. This study applies advanced ensemble methods, such as random forest, boosting, bagging, and stacking, to enhance diabetes onset prediction using a synthetic minority over-sampling technique (SMOTE)-balanced data from the Pima Indians Diabetes Database. The research involves extensive data processing, feature engineering, and cross-validation. Model performance is assessed using several evaluation metrics, such as F1-score and AUC-ROC (Area Under the Curve-Receiver Operating Characteristic), along with accuracy, precision, and recall. The findings indicate that ensemble techniques, especially random forest, bagging, and boosting, surpass traditional models, achieving an accuracy of 88%, recall of 82%, and precision of 85%. These findings emphasize the effectiveness of ensemble learning in enhancing predictive analytics for healthcare, supporting early diagnosis, and personalized patient care. Future research should explore integrating deep learning models with diverse datasets to improve predictive accuracy and generalizability.
Mathematical Modelling of Population Dynamics with Growth Sensitivity of Abuja Federal Territory, Nigeria
Keeping track of the human population is essential for proper planning for facilities such as healthcare, infrastructure, education, and other essential needs. There are various ways by which the government can ensure that service provision is improved and maintained for its citizens and very often this starts by knowing the changes in demography as a function of time. In this work, mathematical modeling and simulations are used to study the population dynamics of Abuja. The models are used for prediction of the population and how its dynamics change over time. With approximate growth rate at 9.3% per annum, the projected population of Abuja will hit 30,220,701 million by the year 2039 all things being equal. Parameter sensitivity analysis was performed using population census data, and the results show a huge influence of variations in the model parameters. The results indicate that the difference between the per capita birth and death rate parameters is crucial for changes in the population. Such findings can also be analogously applied to other cities with a similar population structure and economy.
Artificial Neural Network Regression Modelling of Poverty Index in Nigeria
Due to the benefits of Artificial Neural Network (ANN) regression modelling over classical linear regression estimator with respect to faulty tolerance and generalization ability, the study adopted (ANN) regression modelling in order to investigate the impacts of economic variables indices on the poverty index of Nigeria in the years 2018/2019, artificial neural network regression modelling was adopted. This study examined poverty modelling in the realm of (ANN) regression and showcased the contribution of the weight of each predictor variable towards the nodes that determine the Multidimensional Poverty Index (MPI). Most literatures do not interpret the weights and bias of ANN regression, they only described the architecture of the procedures to obtain it. This is the gap this study filled. The study observed that Food insecurity has the highest relative importance with 0.085 magnitude to (MPI) while sanitation has lowest relative importance with magnitude of 0.045 to (MPI).
Formulating a Linear Model from a One-Way Classification Model
This study introduces a novel approach to formulating a linear regression model using a matrix method for Completely Randomized Design (CRD), a type of One-Way classification. In this approach, treatment is the sole classification, and the formulation utilizes response variables organized into rows and columns. The method yields the number of trials (n), slope, predictor, and regression parameters within the system. To ensure the normality of the response variable and select the appropriate error term distribution, we conducted normality tests (Shapiro-Wilk, Anderson-Darling, Cramér-von Mises, Lilliefors) and exploratory data analysis techniques (histogram, boxplot, QQ-plot). The formulation was validated through illustrations, and the results from the matrix method regression were compared to the ordinary least squares regression, yielding identical values for the regressors, and confirming the robustness of the proposed formulation. Furthermore, we evaluated the performance of machine learning linear regression model, which outperformed ordinary least squares regression in terms of mean absolute error, mean square error, and root mean square error, demonstrating the superior accuracy of the proposed approach.
Multicollinearity in Binomial Regression: A Comparison between Conditional Expectations and Residuals (CERES) and Partial Residual (PR) Plots for Detection
Conditional expectations and residuals (CERES) and partial residual (PR) plots have been used in linear regression model for the identification of multicollinearity. But not much work has been done on how they perform in generalized linear models (GLM). Binomial regression model (BRM) is a very important type of GLM which has wide applications in dealing with heart disease and many other types of data. In this paper we have offered a comparison between CERES and PR plots in BRM to detect the multicollinearity problem. At first, we have developed a comparison tool and then apply them to real-world and simulated data. We observe the performance of these plots on the detection of a possible multicollinearity separately. We observe that both these plots perform well in order to diagnose this problem for a real data. However, the overall performance of the CERES plot is found better as compared to the PR plots
Boosting Cancer Dataset Performance with Mutual Information-Based Feature Prioritization
In the field of statistical modelling, mutual information is a crucial and common concept, suitable for tasks such as selecting the most important features or classifying data into different categories. Feature selection addresses the challenge of high-dimensional data in building effective predictive models by identifying relevant attributes while mitigating the curse of dimensionality. Previous studies have benchmarked the effectiveness of statistical models against established results. To enhance this, a new benchmark method is proposed, exploiting ranking features via mutual information scores. Mutual information score is used to understand the relationship between underlying data and variables. The performance of the classification depends on its information content, which directly affects the performance of the statistical model. The technique simultaneously determines the optimal feature quantity to guide the feature selection process. The validation of these selected features is conducted through Z-score graphs. Experimental results show that this method can identify feature subsets better than using the full features. This advance promises to improve cancer analysis, enabling more sophisticated diagnostic and prognostic methods.
ARIMA Modeling of the Exchange Rate of the Nigerian Naira to the US Dollar with Covid-19 Pandemic Event in Focus
We modelled the average monthly exchange rate between the Nigerian Naira and the USDollar using univariate time series analysis. With a data set covering January 2002 to June 2022, we divided the data set into three distinct periods, vis-à-vis; the Pre COVID-19 pandemic period, the COVID-19 period, and the Combined period (combination of both pre-COVID-19 and COVID-19 periods). This was done with a view to examining the effect of COVID-19 pandemic on the generation mechanism of the Naira/USDollar exchange rate. The Naira/USDollar exchange rate data was found to be non-stationary and was appropriately differenced to attain stationarity. Subsequently, we fitted ARIMA models for each of the three scenarios using Box Jenkins methodology. Finally, the estimated models were used for forecasting. While the estimated models for both the Pre COVID-19 and the Combined periods yielded forecast values that suggest future depreciation of the Naira against the USDollar, that of the COVID-19 period yielded forecast values that suggest future appreciation of the Naira against the USDollar.
On the Type I Half Logistic Lomax Distribution with Applications
In this research, a new four-parameter lifetime distribution called the Type I Half-Logistic exponentiated Lomax distribution is introduced. The study presents various mathematical properties of this novel model, including moments, moment generating function, quantile function, survival function, and hazard function. Additionally, the behavior of the probability density function for both the maximum and minimum order statistics is analyzed within the framework of this new distribution. The paper also explores parameter estimation using the maximum likelihood method to estimate the distribution's unknown parameters. To validate the practicality and versatility of the proposed distribution, three real-life datasets are used for empirical validation. Comparisons are made with existing lifetime distributions from the literature, and the results consistently demonstrate that the proposed distribution outperforms others in termsof fit and flexibility for all three datasets. This highlights its effectiveness in modeling diverse real-world scenarios. The introduction of the Type I Half-Logistic exponentiated Lomax distribution contributes to the expansion of distribution theory and offers a valuable tool for researchers, statisticians, and analysts to accurately model and analyze lifetime data in various fields. The analytical properties derived in this study provide a solid foundation for future research and applications, and the successful parameter estimation and goodness-of-fit results further validate the practical significance of the proposed distribution.
Parameter Estimation for Circular Simultaneous Functional Relationship Model (CSFRM) for Unequal Variances
In this study, we propose an extended model of the Circular Simultaneous Functional Relationship Model from the Circular Functional Relationship Model. In this case, the circular model and the circular variable will be applied assuming the error variances are not equal. All estimations of the parameter followed von Mises distribution. The angular and slope parameters are obtained using the ms function, while concentration parameter estimation is obtained from the polyroot function provided in the SPLUS statistical package. The simulation study has been conducted to assess the efficiency of the proposed model. The simulation results showed that as sample size and concentration parameters increase, all parameters’ estimates are close to the true value and have a smaller bias. The illustration of real wind and wave direction data from two different bases of data is given to show its practical applicability. We note that the proposed method for parameter estimation works well with the proposed model in the case of unequal error variances as it provides good estimates. Keywords: circular simultaneous functional relationship model, ms function, parameter estimates, polyroot function, unequal variances
Copyright (c) 2023 Journal of Statistical Modeling & Analytics (JOSMA)
Nonlinear Autoregressive Neural Network for Forecasting COVID-19 Confirmed Cases in Malaysia
A nonlinear autoregressive neural network (NARNN) model is a feedforward neural network for handling complex nonlinear time series problems. In this study, the tangent sigmoid (tansig) activation function with the different numbers of past values and different numbers of hidden neurons for the NARNN modelis determined. The COVID-19 daily confirmed cases in Malaysia are collected with different amounts of samples used, which are 100, 500 and 900. Therefore, data from 100, 500 and 900 days before 21 September 2022 are extracted for the NARNN model training, validation and testing procedure. The lowest average mean squared error (MSE) becomes the best combination. The result shows that the past value is 1:10 and the number of neurons of 10 when the sample size is 100. At sample size 500, past values of 1:10 and neurons of 8 enable the model to perform at its best. Whereas for sample size 900, the network setting of 1:5 past value and five hidden neurons gives the least MSE. Multi-step ahead time series forecasting is conducted to forecast the number of confirmed COVID-19 cases in 7 days from 22 to 28 September 2022. The result shown for 7-days-ahead confirmed cases indicating Malaysia datasets, the best forecasting outcome occurs when 900 samples are inputted.
Predicting Indonesia’s Gross Domestic Product (GDP): A Comparative Analysis of Regression and Machine Learning Models
This research paper presents an analysis of Indonesia's quarterly Gross Domestic Product (GDP) growth spanning a significant 13-year period, from the first quarter of 2010 to the fourth quarter of 2022. The study focuses on utilizing four key economic indicators to gain insights into the country's economic performance during this timeframe. To develop accurate predictive models, we utilize Multiple Linear Regression (MLR), K-Nearest Neighbours (K-NN), and Artificial Neural Network (ANN) approaches. The models are compared based on performance metrics, including the Mean Absolute Error (MAE) and the Root Mean Squared Error (RMSE). Our findings indicate that the MLR model outperforms the machine learning models in forecasting Indonesia's GDP. The MLR model is approximately 93.3% better than the K-NN model and approximately 64.7% better than the ANN model based on the RMSE values. This suggests that a simpler and more explainable model, such as MLR, suffices to provide meaningful and interpretable results. The paper's insights are valuable to economists, policymakers, and researchers, offering a practical and understandable means to predict Indonesia's economic trajectory. Keywords: Artificial Neural Network (ANN), Gross Domestic Product (GDP), K-Nearest Neighbours (K-NN), Multiple Linear Regression (MLR), Predictive Models.
Bayesian Restricted Stein-Rule Least Squares with N on-Spherical Disturbances
Bayesian restricted Stein-rule least squares is a novelty estimator introduced to obtain parameters of the family of restricted least squares with intertwined heteroscedastic and autocorrelated disturbance errors. Errors in regression modelling set to measure the quality of the data and/or model of which intertwining of most prominent errors disturbances play a significant role in determining the quality of the data and/or model. Efforts were geared towards the comparison of the performances and relative efficiency of the small sample property of Bayesian and classical family of restricted Stein-rule least squares. The sample size was set at 25 to capture the intertwined disturbance errors, and the iteration of the Monte Carlo simulation was set at 10000 for both classical and Bayesian paradigms. The ρ and ∅ for both Autoregressive of order one (AR(1)) and Moving average of order one (MA(1)) processes respectively were set asymmetrically as -0.8,-0.5,-0.3, 0,0.3,0.5 and 0.8 while δ as a scale of heteroscedasticity was set at 0-homoscedasticity, -0.3-mild,-0.5-moderate and 1-severe. The outcome of the study pointed out that Bayesian estimation (posterior mean and Bayes estimates) for restricted least squares and restricted Stein-rule estimators outperformed classical restricted and restricted Stein-rule estimators both in performances and relative efficiency measurements. It is therefore recommended to make use of the Bayesian framework when encountering similar disturbances in a small sample size.
The Use of Standardized Exponentiated Gumbel Error Innovation Distribution to Forecast Volatility: A Comparative Study
This study is designed to model several selected volatility models using a newly developed errorinnovation distribution called Standardized Exponentiated Gumbel Error Innovation Distribution (SEGEID) to determine the efficiency and effectiveness of the model in terms of its adaptability and forecast evaluation. SEGEID improves some existing error distributions and uses the standard&Poor-500 index data returned from 2004 to 2022.The use of this error innovation distribution, GJR-GARCH (1,1), has been shown to be more effective than other volatility models considered in this study. The results of the study show that GJR-GARCH (1,1) is better than GARCH (1,1), EGARCH (1,1) and TGARCH (1, 1) because it has the lowest AIC(-17.0394) and RMSE (0.00011).
Diagnosing Sexually Transmitted Disease from Some Symptoms Using Machine Learning Models
Sexually transmitted disease (STDs) is of serious concern, especially among the youths. Efforts to eradicate such diseases are often frustrated due to different sociodemographic elements and clinical factors that usually lead to misdiagnosis. Thus, this paper applied six (6) distinct machine learning models to accurately analyze STD infections reported by 400 patients that attended Federal Polytechnic Ilaro Medical Center, Ogun State Nigeria. Weak non-significant correlations were obtained between the 7 symptoms considered for the diagnoses and the diagnosis outcome, but no significant pattern was observed. However, the application of data mining tools revealed a hidden pattern that correctly predicted the outcome using the subjects’ symptoms, age, and sex. 4 out of the 6 machine learning models were adjudged to perform well using different performance metrics, of which Logistic regression model was found to be the best. The model feature importance chat shows that Vagina Discharge and Vagina itching have the highest and almost the same level of impact on the possibility of a diagnosed patient having STDs. Furthermore, a 100% performance of logistic regression implies that the model correctly predicted all the 309 true negatives and 101 true positives with a misclassification (misdiagnosis) of zero.
Monte Carlo Experiment on the Asymptotic Efficiency of System Estimators under Multicollinearity
Classical linear regression model assumes that there is no multicollinearity among the explanatory variables in a regression model. Contrary to this assumption, where multicollinearity is perfect, the regression coefficients of the explanatory variables are indeterminable and their standard errors are infinite. On the other hand, where multicollinearity isless than perfect, the regression coefficients, although determinable, possess large standard errors. This implies that the coefficients cannot be estimated with great precision. Hence multicollinearity problem is a major problem in econometric analysis. Using Monte Carlo Simulation, we evaluated the asymptotic efficiency of six estimators (OLS, ILS, 2SLS, 3SLS, LIML and FIML), under different magnitudes of the unintended linear relationship between the exogenous variables. Using the SSR criteria, we found that OLS followed by ILS turned out the best estimates amongst the six estimators under multicollinearity. We also found that with increasing sample size, there is no remarkable asymptotic effect in the performance of the estimators at the levels of multicollinearity.
THE EXTENDED ODD FRÉCHET INVERTED EXPONENTIAL DISTRIBUTION: PROPERTIES AND APPLICATION
This research introduces a new three-parameter continuous distribution from the extended odd Fréchet (EOF) family of distributions called the extended odd Fréchet inverted exponential (EOFIE) distribution. Derivations of EOFIE distribution statistical properties are derived and its parameters are estimated using the technique of maximum likelihood (ML) estimation. The ML estimates finite sample performance of the EOFIE distribution is evaluated through simulation. Results obtained from the application of the EOFIE distribution to two real-life datasets showed that it provides a better fit than other competing distributions.
A New Extended Exponentiated Exponential Distribution with Application to Truncated Life Tests
In this paper, the Extended Exponentiated Exponential distribution was developed from the New Extended Exponentiated-G family of distributions. Some mathematical properties of the newly derived distribution such as moment, moment generating function, quantile function, hazard function, survival function, odd function, distribution of order statistic and maximum likelihood estimation were all derived. An acceptance sampling plan based on truncated life tests was also developed for the distribution. The minimum size, operating characteristic function values and minimum ratio of the sampling plan required to confirm the specified mean life are also presented. Real-life example was used to illustrate the results. A comparison study with different sampling plans and distributions by earlier authors is also carried out to demonstrate the advantage of the new plan over existing plans. The findings revealed that the proposed plan is better and can be conveniently useful by researchers and Quality Control Personnel in product acceptance sampling inspection.
Susceptible Infectious and Recovered Model for Severity of COVID-19 Pandemic in Nigeria
The COVID-19 pandemic has posed a serious threat to the lives of many people across the globe. In Nigeria, the first COVID-19 case was reported on 27th February, 2020, after an Italian citizen tested positive for Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19. The spread of SARS-CoV-2 remained stable until 21st April, 2020, where the number of infections in three digits emerged and as of June 20th 2020, Nigeria has a total of 19,808 confirmed cases, total discharged 6,718 and 506 deaths recorded. The severity of SARS-CoV-2 by far surpasses the coronaviruses that were so far discovered in 2003 and 2012. In this study, a Susceptible Infectious and Recovered (SIR) model for severity of COVID-19 pandemic in Nigeria has been developed to obtain the overall picture of the severity of the disease in Nigeria. Based on the simulation of the fitted model, we arrived at a basic reproductive number (R0) 1.533931 and an infected individual required 9 days to recover from the disease. The findings indicated that the fitted model satisfactorily mimicked the actual data reported by the Nigeria Center for Disease Control (NCDC). The R0 obtained showed that on the average one infected individual would spread the COVID-19 to two susceptible individuals. Though the pandemic is under control, but the government needs to take measures to totally contain the spread of the virus.
Parameter Estimation for Balance-Equal Replicated Linear Functional Relationship Model: An Application to the Wind Directional Data
A linear functional relationship model can be used to model the relationship between two circular variables when both variables are observed with errors. When the ratio of error concentration parameter is unknown, it is suggested that the replicated linear functional relationship model be used. The purpose of this paper is to present all the parameter estimates of the replicated linear functional relationship model for balance replicates and equal circular variables. Simulations studies are performed to study understand the behavior of the parameter estimators for the balance-equal replicated linear functional relationship model. The empirical results obtained suggest that the proposed parameter estimation method performs well with small bias. A simple application of the model is demonstrated by analyzing a real dataset of a wind directional data.