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A Bayesian Hierarchical Cox Model with Elastic Net Regularization for Improved Survival Prediction and Feature Selection
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02-25) Bulus I. Doroh; Kazeem A. Dauda; Rasheed K. Lamidi
In recent years, the growing availability of large-scale data across a wide range of disciplines has created new opportunities for developing models that improve the predictive accuracy of statistical models. Although techniques such as regularization and Bayesian hierarchical methods are commonly used for building predictive models, substantial challenges remain, particularly when dealing with high-dimensional datasets that contain considerable noise. In this study, we propose a Bayesian hierarchical model that employs a spike-and-slab hierarchical elastic net prior that regularizes the Cox Proportional Hazards (Cox-PH) model. The method combines Bayesian modeling with the regularized partial log-likelihood of the Cox-PH framework, incorporating an Elastic Net penalty to estimate the joint posterior distribution under a hierarchical elastic net prior. We compute this posterior using an Expectation–Maximization Cyclic Coordinate Descent Algorithm (EM-CCDA), which streamlines feature selection and enhances overall predictive performance. We evaluate the algorithm’s performance through Monte Carlo simulations and apply it to three real-world datasets, comparing the results with those from established classical and Bayesian survival analysis approaches. The findings demonstrate notable gains in both feature selection and predictive accuracy, highlighting the model’s strong ability to predict patient survival and identify relevant genes in real biological datasets.
A Comparative Analysis of Semiparametric Tests for Fractional Cointegration in Panel Data Models
(Austria Statistical Society, 2022) Saidat Fehintola Olaniran; Mohd Tahir Ismail
Several authors have studied fractional cointegration in time series data, but little or no consideration has been extended to panel data settings. Therefore, in this paper, we compare the finite sample behaviour of existing fractional cointegration time-series test procedures in panel data settings. This comparison is performed to determine the best tests that can be adapted to fractional cointegration in panel data settings. Specifically, simulation studies and real-life data analysis were performed to study the changes in the empirical type I error rate and power of six semiparametric fractional cointegration tests in panel settings. The various results revealed the limitations of the tests in the nonstationary and low or high correlation of the residual errors conditions. Also, two of the test procedures were recommended for testing the null hypothesis of no fractional cointegration in both time series and panel data settings.
A Comparative Study on Zero-truncated Generalized Poisson-Lindley and Zero-truncated Poisson-Lindley Distributions
(International journal of Mathematical Archive (IJMA), 2017) Aderoju, S.A.; Jolayemi, E.T.; Ibrahim, A.O.
In this paper, Zero-truncated Com-binomial distribution was derived and investigated its behavior in modeling structurally non-zero data. The proposed distribution is characterized by two parameters, which make it flexible. The maximum likelihood method is used to obtain the estimators of the parameters through R-software. Two real-life datasets were used to evaluate its performance. The statistic (chi square goodness-of-fit) with the p-value shows that the proposed Zero-truncated Com-binomial distribution yields “a good fit”.
A GENERALIZED SCHEME FOR THE NUMERICAL SOLUTION OF INITIAL VALUE PROBLEMS IN ORDINARY DIFFERENTIAL EQUATIONS BY THE RECURSIVE FORMULATION OF TAU METHOD
(2013-05) K. Issa; R.B. Adeniyi
The generalization of the recursive form of the tau method for both overdetermined and non-overdetermined ordinary differential equations of the initial value type is the main thrust of the work reported here. This will facilitate an automation of this variant of the method and consequently an efficient utilization of the technique. Results from the numerical experiment confirm the validity and effectiveness of the derived scheme.
A HYBRID EXPONENTIAL-GENERALIZED GAMMA DISTRIBUTION WITH MEAN BASES MIXING PROPORTION: THEORY AND APPLICATIONS
(FUDMA Journal of Sciences (FJS), 2026-02-04) Uchechukwu Kalu, Samuel Adewale Aderoju, Bello Ishola Sanni, Toheeb Akorede Yussuf, Adediran Dauda Adeshola and Saheed Kunle Ajibade
In our study, we propose the Exponential-Generalized Gamma Distribution (EGGD) with Mean-Based Mixing Proportion. A new hybrid survival distribution developed to overcome the limitations of existing parametric models in modeling complex hazard functions and structures. The EGGD combines the simplicity of the exponential distribution with the flexibility of the generalized gamma distribution. The analytical calculations of the distribution’s important statistical properties, namely moments, skewness, kurtosis, survival, and hazard functions, have been derived to provide further insights into the distribution’s behavior. The EGGD parameter estimation is conducted using maximum likelihood estimation (MLE). The performance of the maximum likelihood estimates was rigorously examined through a Monte Carlo simulation study. The performance measures used in the study were bias and MSE. The practicality of the Model was examined through its application to real-world lifetime data. Its performance was compared with that of other existing three-parameter and two-parameter lifetime distributions. The model adequacy is assessed using information criteria, including AIC, AICc, HQIC, and BIC. Across three datasets, the EGGD consistently exhibits superior goodness-of-fit compared to the other considered models, highlighting its flexibility and robustness as a tool for survival and reliability analysis.
A Hybrid Exponential-Generalized Gamma Distribution with Mean Bases Mixing Proportion: Theory and Applications
(FUDMA JOURNAL OF SCIENCES, 2026-04-01) Kalu, U., Aderoju, S. A., Sanni, B. I., Yussuf, T. A., Adeshola, A. D., & Kunle, S. A.
In our study, we propose the Exponential-Generalized Gamma Distribution (EGGD) with Mean-Based Mixing Proportion. A new hybrid survival distribution developed to overcome the limitations of existing parametric models in modeling complex hazard functions and structures. The EGGD combines the simplicity of the exponential distribution with the flexibility of the generalized gamma distribution. The analytical calculations of the distribution’s important statistical properties, namely moments, skewness, kurtosis, survival, and hazard functions, have been derived to provide further insights into the distribution’s behavior. The EGGD parameter estimation is conducted using maximum likelihood estimation (MLE). The performance of the maximum likelihood estimates was rigorously examined through a Monte Carlo simulation study. The performance measures used in the study were bias and MSE. The practicality of the Model was examined through its application to real-world lifetime data. Its performance was compared with that of other existing three parameter and two-parameter lifetime distributions. The model adequacy is assessed using information criteria, including AIC, AICc, HQIC, and BIC. Across three datasets, the EGGD consistently exhibits superior goodness-of-fit compared to the other considered models, highlighting its flexibility and robustness as a tool for survival and reliability analysis.
A New Generalized Gamma-Weibull Distribution and its Applications
(Al-Bahir Journal for Engineering and Pure Sciences, 2023-04-17) Aleshinloye, N.I.; Aderoju, S. A.; Abiodun, A.A.; Taiwo, B.L.
In this paper, a New Generalized Gamma-Weibull (NGGW) distribution is developed by compounding Weibull and generalized gamma distribution. Some mathematical properties such as moments, Renyi entropy and order statistics are derived and discussed. The maximum likelihood estimation (MLE) method is used to estimate the model parameters. The proposed model is applied to two real-life datasets to illustrate its performance and flexibility as compared to some other competing distributions. The results obtained show that the new distribution fits each of the data better than the other competing distributions.
A New Generalized Gamma-Weibull Distribution with Applications to Time-to-event Data
(2023-11-18) Kazeem Adesina Dauda; Rasheed Kehinde Lamidi; Adeshola Adediran Dauda; Waheed Babatunde Yahya
In this research, a new class of probability distributions referred to as Generalized Gamma Weibull (GGW) distributions was introduced within the context of parametric survival analysis. This distribution represents a modification of the gamma Weibull distribution and offers valuable insights, particularly when dealing with highly skewed lifetime data. The study extensively examined the mathematical characteristics of these distributions, encompassing hazard functions, moments, quantile functions, and order statistics. Furthermore, the research delved into parameter estimation methods for these newly proposed distributions, employing the maximum likelihood technique, Fisher Information (FI), and deriving asymptotic confidence intervals for both censored and uncensored scenarios. To illustrate the practical utility of these proposed distributions, the study applied them to analyze two sets of real-life survival data and two sets of real-life data, resulting in a total of four distinct datasets. To gauge the effectiveness of the GGW distributions in comparison to existing methods such as Generalized Weibull and Generalized gamma (G-Weibull and G-Gamma) distributions, the research employed statistical indices including the Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), and Bayesian Information Criterion (BIC). The outcomes of this comparative analysis demonstrated the superior performance of the newly introduced GGW distributions (AIC=338.6313, BIC=346.2794, and CAIC=339.5202) when contrasted with the existing methods (G-Weibull: AIC=376.1946, BIC=381.9307, and CAIC=376.5424) across all three criteria, thereby highlighting the enhanced suitability of GGW distributions for modeling and analyzing skewed lifetime data.
A new generalized Poisson mixed distribution and its application
(Applied Mathematical Sciences, 2020) Aderoju, S.A.
A new generalized Poisson mixed distribution is proposed in this study called New Generalized Poisson-Sujatha distribution (NGPSD). The properties and application of the distribution are studied. The two parameter distribution is obtained by compounding Poisson distribution with a two parameter generalized Sujatha distribution. The distribution has a tendency to account for over-dispersion in count data. The first four moments, variance and coefficient of variation of the distribution are also obtained. The estimators of its parameters are obtained via maximum likelihood method using R-software. The goodness-of-fit of the distribution is compared with other distributions such as Poisson distribution (PO), negative binomial (NB), Generalized Poisson-Lindley (GPL) and a New Generalized Poisson-Lindley (NGPL) Distributions. It can be seen that the test statistic, AIC and BIC for the NGPSD are lower than those of competing distributions implying that the proposed distribution satisfactorily fits better to the data set.
A New Lifetime Distribution and its Application to Cancer Data
(Journal of Biostatistics and Epidemiology, 2023-12-15) Samuel Adewale Aderoju; Nihimat Iyebuhola Aleshinloye; Bako Lukmon Taiwo; Bello Ishola Sanni
Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice. Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.
A New Lifetime Distribution and its Application to Cancer Data.
(Journal of Biostatistics and Epidemiology., 2023) Aderoju, S.A.; Aleshinloye, N.I.; Taiwo, B.L. and Sanni, B.I.
Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice. Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models
A novel hybrid dimension reduction technique for efficient selection of bio-marker genes and prediction of heart failure status of patients
(Scientific African, 2021-05-02) Kazeem Adesina Dauda; Kabir Opeyemi Olorede; Samuel Adewale Aderoju
This study highlighted and provided a conceptual framework of a hybridized dimension reduction by combining Genetic Algorithms (GA) and Boruta Algorithm (BA) with Deep Neural Network (DNN). Among questions left unanswered sufficiently by both computational and biological scientists are: which genes among thousand of genes are statistically relevant to the prediction of patients’ heart rhythm? and how they are associated with heart rhythm? A plethora of models has been proposed to reliably identify core informative genes. The premise of this present work is to address these limitations. Five distinct micro-array data on heart diseases have been taken into consideration to observe the prominent genes. We form three distinct set two-way hybrids between Boruta Algorithm and Neural Network (BANN); Genetic Algorithm and Deep Neural Network (GADNN) and Boruta Algorithm and Deep Neural Network (BADNN), respectively, to extract highly differentially expressed genes to achieve both better estimation and clearer interpretation of the parameters included in these models. The results of the filtering process were observed to be impressive since the technique removed noisy genes. The proposed BA algorithm was observed to select minimum genes in the wrapper process with about 80% of the five datasets than the proposed GA algorithm with 20%. Moreover, the empirical comparative results revealed that BADNN outperformed other proposed algorithms with prediction ac curacy of 97%, 87%, and 100% respectively. Finally, this study has successfully demonstrated the utility, versatility, and applicability of hybrid dimension reduction algorithms (HDRA) in the realm of deep neural networks.
A Novel Hybrid Dimension Reduction Technique for Efficient Selection of Bio-marker Genes and Prediction of Heart Failure Status of Patients
(Elsevier, 2021) Dauda K. A.; Olorede K. O.; Aderoju S. A.
This study highlighted and provided a conceptual framework of a hybridized dimension reduction by combining Genetic Algorithms (GA) and Boruta Algorithm (BA) with Deep Neural Network (DNN). Among questions left unanswered sufficiently by both computational and biological scientists are: which genes among thousand of genes are statistically relevant to the prediction of patients’ heart rhythm? and how they are associated with heart rhythm? A plethora of models has been proposed to reliably identify core informative genes. The premise of this present work is to address these limitations. Five distinct micro-array data on heart diseases have been taken into consideration to observe the prominent genes. We form three distinct set two-way hybrids between Boruta Algorithm and Neural Network (BANN); Genetic Algorithm and Deep Neural Network (GADNN) and Boruta Algorithm and Deep Neural Network (BADNN), respectively, to extract highly differentially expressed genes to achieve both better estimation and clearer interpretation of the parameters included in these models. The results of the filtering process were observed to be impressive since the technique removed noisy genes. The proposed BA algorithm was observed to select minimum genes in the wrapper process with about 80% of the five datasets than the proposed GA algorithm with 20%. Moreover, the empirical comparative results revealed that BADNN outperformed other proposed algorithms with prediction accuracy of 97%, 87%, and 100% respectively. Finally, this study has successfully demonstrated the utility, versatility, and applicability of hybrid dimension reduction algorithms (HDRA) in the realm of deep neural networks.
A Novel Parsimonious Gamma Mixture with Applications to Reliability Data
(Journal of Basics and Applied Sciences Research, 2026-01-30) *Aderoju, S.A.*, Adesina, B., Olasupo, A., Sanni, B.I., Kalu, U., Olaboye, D., and Yussuf, T.A.
Lifetime distributions are salient statistical tools to model the different characteristics of lifetime datasets. The statistical literature contains very modern distributions to analyze these kinds of datasets. Nonetheless, these distributions have many parameters, which cause a problem in the estimation step. To offer fresh possibilities in modeling these kinds of datasets, we propose a Parsimonious Gamma Mixture (PGM) distribution using a finite mixture of Gamma distributions with parameter-dependent mixing weights. The proposed distribution has only one parameter and simple mathematical forms. The mathematical properties of the distributions, including moments, reliability functions and order statistics, are studied in detail. The unknown model parameter is estimated by using the maximum likelihood. The extensive simulation study is used to study the performance of parameter estimation. To convince the readers in favour of the proposed distribution, three real datasets from engineering and materials science are analyzed and compared with competitive models. Empirical findings show that the proposed one-parameter lifetime distribution produces better results than the other similar existing distributions. Its consistent achievement of the lowest AIC and BIC values across all datasets confirms its enhanced ability to capture diverse data patterns with remarkable parameter parsimony, making it a highly effective tool for modelling reliability and survival data.
A Novel Variable Selection Procedure for Binary Logistic Regression Using Akaike Information Criteria Testing: An Example in Breast Cancer Prediction.
(Turkiye klinikleri, 2023-07-13) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran
Breast cancer is a leading cause of cancer-related death among women worldwide, with approximately 2.3 million new cases and 685,000 deaths reported in 2020 alone. One critical step in developing effective classification and prediction models is variable selection, which involves identifying a subset of relevant variables from a larger set of potential predictors. Accurate variable selection is crucial for building interpretable and robust models that are not overfit to noise, leading to improved model performance and generalization ability. In this paper, we proposed an alternative objective approach for comparing two Akaike Information Criterions (AIC) that originated from two competing models, such that the magnitude of the difference is subjected to the statistical test of significance. Material and Methods: We developed a new backward elimination variable selection procedure similar in spirit to the existing “step AIC” within the environment of R statistical software. We used both simulated and Wisconsin breast cancer diagnostic datasets to compare the proposed method's variable selection and predictive performances with “step AIC” and LASSO. Results: The simulation showed that the proposed AIC procedure achieved higher variable selection sensitivity, specificity and accu racy when compared to stepAIC and LASSO. Also, the proposed AIC method's prediction results are relatively comparable with ste pAIC and LASSO at various simulated data dimensions. Similar supremacy results were observed with the breast cancer dataset used. Conclusion: The AIC-based variable selection approach pro posed is a promising method that integrates AIC with statistical testing for improved variable selection in breast cancer classifica tion and predictio
A Numerical Investigation of Solar Thermal Radiation and Non-Fourier Heat Conduction in Solar Aircraft Wings Using Ternary Hybrid Nanofluids
(Alicon Publications, 2026-03-18) Adeshola, A. D., Jimoh, A. K., Ajibade, S. K., Isola, A. A. and Obalalu, A. M.
The aim of this study is to enhance the operating thermal efficiency of solar aircraft through the use of solar thermal radiation and ternary hybrid nanofluids (THNF). The study inspects the convective thermal transfer in wings of solar aircraft using a noble innovative THNF which consists of has Copper (Cu), SiO2 Silicon dioxide, Zirconium dioxide (Zr02) nanoparticles and in Propylene glycol (C3H8O2) as the basic fluid. The model used to analyze the problem uses a parabolic trough solar collector (PTSC) to model the solar thermal radiation and makes use of Cattaneo-Christov heat flux model in order to account for the non-Fourier heat conduction phenomena. Wavelets and Gegenbauer wavelets methods was uses to solve the system of ODEs. The study results display that the improvement of thermal transport via THNF is due to the advanced thermal conductivity of the nanoparticles and elevated capability of the THNF to collect and absorb solar power energy. The Cattaneo-Christov model offers a better representation of heat flux with the incorporation of thermal time delay resulting in the enhanced prediction of thermal behavior in the aircraft wings. Also, PTSC is significantly beneficial in the enhancement of the thermal management procedure of the solar energy harvesting process.
A Numerical Investigation of Solar Thermal Radiation and Non-Fourier Heat Conduction in Solar Aircraft Wings Using Ternary Hybrid Nanofluids
(International Journal of Advanced Engineering and Management Research, 2026-03-18) Adediran Dauda Adeshola, A. K. Jimoh, Ajibade Saheed Kunle, Ishola Abdulmuiz Adeshina, A. M Obalalu
The aim of this study is to enhance the operating thermal efficiency of solar aircraft through the use of solar thermal radiation and ternary hybrid nanofluids (THNF). The study inspects the convective thermal transfer in wings of solar aircraft using a noble innovative THNF which consists of has Copper (Cu), SiO2 Silicon dioxide, Zirconium dioxide (Zr02) nanoparticles and in Propylene glycol (C3H8O2) as the basic fluid. The model used to analyze the problem uses a parabolic trough solar collector (PTSC) to model the solar thermal radiation and makes use of Cattaneo-Christov heat flux model in order to account for the non-Fourier heat conduction phenomena. Wavelets and Gegenbauer wavelets methods was uses to solve the system of ODEs. The study results display that the improvement of thermal transport via THNF is due to the advanced thermal conductivity of the nanoparticles and elevated capability of the THNF to collect and absorb solar power energy. The Cattaneo-Christov model offers a better representation of heat flux with the incorporation of thermal time delay resulting in the enhanced prediction of thermal behavior in the aircraft wings. Also, PTSC is significantly beneficial in the enhancement of the thermal management procedure of the solar energy harvesting process.
A Test Procedure for Ordered Hypothesis of Population Proportions Against a Control
(Turkiye Klinikleri Journal of Biostatistics, 2016) Yahya W. B.; Olaniran O. R.; Garba M. K.; Oloyede I.; Banjoko A. W.; Dauda K. A.; Olorede K. O.
Objective: This paper aims to present a novel procedure for testing a set of population proportions against an ordered alternative with a control. Material and Methods: The distribution of the test statistic for the proposed test was determined theoretically and through Monte-Carlo experiments. The efficiency of the proposed test method was compared with the classical Chi-square test of homogeneity of population proportions using their empirical Type I error rates and powers at various sample sizes. Results: The new test statistic that was developed for testing a set of population proportions against an ordered alternative with a control was found to have a Chi-square distribution with non-integer values degrees of freedom v that depend on the number of population groups k being compared. Table of values of v for comparing up to 26 population groups was constructed while an expression was developed to determine v for cases where k > 26. Further results showed that the new test method is capable of detecting the superiority of a treatment, for instance a new drug type, over some of the existing ones in situations where only the qualitative data on users' preferences of all the available treatments (drug types) are available. The new test method was found to be relatively more powerful and consistent at estimating the nominal Type I error rates (α), especially at smaller sample sizes than the classical Chi-square test of homogeneity of population proportions. Conclusion: Conclusion: The new test method proposed here could find applications in pharmacology where a newly developed drug might be expected to be more preferred by users than some of the existing ones. This kind of test problem can equally exist in medicine, engineering and humanities in situations where only the qualitative data on users' preferences of a set of treatments or systems are available.
A two-step block method with two hybrid points for the numerical solution of first order ordinary differential equations
(2022-12-31) AbdulAzeez Kayode Jimoh; Adebayo Olusegun Adewumi
A continuous two-step block method with two hybrid points for the numerical solution of first order ordinary differential equations is proposed. The approximate solution in form of power series and its first ordered derivative are respectively interpolated at the point x = 0 and collocated at equally spaced points in the interval of consideration. The application of the method involves using the main scheme derived together with the additional schemes simultaneously to obtain the solution to the problem at the grid points. The analysis of the method and the results obtained from the examples considered show that the method is consistent, zero-stable, convergent and of high accuracy.
An algorithm for choosing best shape parameter for numerical solution of partial differential equation via inverse multiquadric radial basis function
(2020-04-30) Kazeem Issa; Sulaiman M. Hambali; Jafar Biazar
Radial Basis Function (RBF) is a real valued function whose value rests only on the distance from some other points called a center, so that a linear combination of radial basis functions are typically used to approximate given functions or differential equations. Radial Basis Function (RBF) approximation has the ability to give an accurate approximation for large data sites which gives smooth solution for a given number of knots points; particularly, when the RBFs are scaled to the nearly flat and the shape parameter is chosen wisely. In this research work, an algorithm for solving partial differential equations is written and implemented on some selected problems, inverse multiquadric (IMQ) function was considered among other RBFs. Preference is given to the choice of shape parameter, which need to be wisely chosen. The strategy is written as an algorithm to perform number of interpolation experiments by changing the interval of the shape parameters and consequently select the best shape parameter that give small root means square error (RMSE). All the computational work has been done using Matlab. The interpolant for the selected problems and its corresponding root means square errors (RMSEs) are tabulated and plotted.

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