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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 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 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 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 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 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.
An analogue of the tau method for ordinary differential equation
(2010) K. Issa; R. B. Adeniyi
In this paper, we construct three orthogonal polynomials which are incorporated into the perturbation term of a numerical scheme analogue to the Ortiz recursive formulation of the Lanczos tau method. The method was implemented on some selected problems and the accuracy obtained justifies the desirability of the numerical scheme.
An error estimation of a numerical scheme analogue to the tau method for initial value problems in ordinary differential equations
(2014-03) K. ISSA; A. K. JIMOH
In a recent paper, we constructed three classes of orthogonal polynomials for use in the perturbation term of a numerical integration scheme analogues to the tau method of Lanczos and Ortiz for ordinary differential equations. The resulting n-th degree approximant y_n(x) of the solution y(x) of the differential equation was accurate and hence justified the scheme. In this present paper, we report an error estimation of the method based on our earlier work. The estimate obtained is good as it correctly captured the order of the tau approximant.
An Exploratory Analysis of Human Immunodeficiency Virus (HIV) Prevalence in Nigeria
(2023) Saidat Fehintola Olaniran; Risikat Ayodeji Bello
This paper involved conducting a statistical analysis on the number of individuals living with HIV/AIDS. The University of Ilorin Teaching Hospital (UITH) was chosen as the case study for the years 2014 to 2019, with factors such as year, sex, and age group taken into account. During this six-year period, a total of 2604 cases were recorded at UITH. Among both sexes, females had the highest number of people living with HIV/AIDS. Additionally, the age group of 31-45 had the highest number of individuals affected by the disease. In terms of the specific year, 2016 had the highest number of people living with the disease, totalling 460 cases. A chi-square test of independence was conducted to examine the relationship between the factors, using a significance level of 0.05. The results indicated that all the considered factors were not independent of each other, meaning they were related.
An Exploratory Analysis of Human Immunodeficiency Virus (HIV) Prevalence in Nigeria.
(Scretech, 2023-06) Saidat Fehintola Olaniran; Risikat Ayodeji Bello
This paper involved conducting a statistical analysis on the number of individuals living with HIV/AIDS. The University of Ilorin Teaching Hospital (UITH) was chosen as the case study for the years 2014 to 2019, with factors such as year, sex, and age group taken into account. During this six-year period, a total of 2604 cases were recorded at UITH. Among both sexes, females had the highest number of people living with HIV/AIDS. Additionally, the age group of 31-45 had the highest number of individuals affected by the disease. In terms of the specific year, 2016 had the highest number of people living with the disease, totaling 460 cases. A chi-square test of independence was conducted to examine the relationship between the factors, using a significance level of 0.05. The results indicated that all the considered factors were not independent of each other, meaning they were related.
Application of Linear Programming on Cost Minimization of Pig Feeds (A Case Study of MOV Farms)
(Earthline Publisher, India, 2023-07-02) T.O. Aliu; M.G. Gbolagade; A.D. Adeshola
This paper discusses cost minimization for pig diet formulation. A linear programming model was formulated for minimum cost and maximum shelf life feed quality. Linear programming technique via MATLAB was employed to obtain solution using real life data collected from MOV Farms, Ilorin. The results, attained at the 7 th iteration gave an optimal value of the objective function obtained as #94 , 226 per 100 kg, with the corresponding values 1.5714, 0.6929, 2.8929, 0 and 0 for x 1 (pig weaner), x 2 (pig growers), x 3 (pig finishers), x 4 (pregnant sow), x 5 (lactating sow) recorded respectively. Hence, the cost of pig feeds formulation can be optimized effectively
Application of linear programming problem on cost minimization on fish feeds
(African Journal online, 2011) A.O. Adeoye; A.K. Dotia; M.O. Agboola; T.O. Aliu
This research, application of linear programming problem on cost minimization on fish feeds was aimed to minimize the cost of production of fish feeds. The data used was collected using both primary and secondary data. Linear programming problem was used to analyzed the data and the optimum solution was obtained at 5th iterations with fingerlings feeds to be 8/9 of tons and growers feeds to be 10/9 tons and the minimum cost of producing the tones of fingerlings and growers is N498, 675.60. We then recommend that any fish farmer who really wants to embark on efficient and effective fish production should use linear programming problem to determine the minimums cost of production. In other to maximizes their profits
Approximate analytical solution of fractional-order generalized integro-differential equations via fractional derivative of shifted Vieta-Lucas polynomial
(Nigerian Society of Physical Sciences, 2024-12-21) Kazeem Issa; Risikat A. Bello; Usman Jos Abubakar
In this paper, we extend fractional-order derivative for the shifted Vieta-Lucas polynomial to generalized-fractional integro-differential equations involving non-local boundary conditions using Galerkin method as transformation technique and obtained N - \delta + 1 system of linear algebraic equations with \lambda i, i = 0, . . . , N unknowns, together with \delta non-local boundary conditions, we obtained (N + 1)- linear equations. The accuracy and effectiveness of the scheme was tested on some selected problems from the literature. Judging from the table of results and figures, we observed that the approximate solution corresponding to the problem that has exact solution in polynomial form gives a closed form solution while problem with non-polynomial exact solution gives better accuracy compared to the existing results.
Approximate analytical solution of fractional-order generalized integro-differential equations via fractional derivative of shifted Vieta-Lucas polynomial
(2024) Kazeem Issa; Risikat A. Bello; Usman Jos Abubakar
In this paper, we extend fractional-order derivative for the shifted Vieta-Lucas polynomial to generalized-fractional integro-differential equations involving non-local boundary conditions using Galerkin method as transformation technique and obtained N - \delta + 1 system of linear algebraic equations with \lambda i, i = 0, . . . , N unknowns, together with \delta non-local boundary conditions, we obtained (N + 1)- linear equations. The accuracy and effectiveness of the scheme was tested on some selected problems from the literature. Judging from the table of results and figures, we observed that the approximate solution corresponding to the problem that has exact solution in polynomial form gives a closed form solution while problem with non-polynomial exact solution gives better accuracy compared to the existing results.
Approximate Analytical Solutions of the Improved Tietz and Improved Rosen-Morse Potential Models
(2015-06) Yahya Wasiu Akanni; Issa Kazeem
In this article, the approximate analytical solutions of the improved Tietz and improved Rosen-Morse potential models are obtained using the parametric Nikiforov-Uvarov method. The energy eigenvalues and wave functions are obtained in the relativistic realm by solving the Klein-Gordon equation. Numerical results of the energy eigenvalues are obtained and studied.
APPROXIMATE SOLUTION OF NON-HOMOGENEOUS PARTIAL DIFFERENTIAL EQUATION VIA IMQ RADIAL BASIS FUNCTION
(Mathematical Association of Nigeria, 2017-12) Issa, K.; Yusuf, K. N.
In recent years radial basis functions (RBFs) has play an important role in approximating functions, partial differential equations (PDEs). In this paper, the problem of solving non-homogeneous partial differential equations subject to the boundary conditions to approximate the solution of PDEs using inverse multiquadric radial basis function (IMQRBF). The results of numerical experiments are presented, and compared with the exact solutions to confirm the effectiveness and the accuracy of the scheme. All the computation was carried out using MATLAB codes.
Approximate Solution of Perturbed Volterra-Fredholm Integrodifferential Equations by Chebyshev-Galerkin Method
(Hindawi, 2017-01-12) K. Issa; F. Salehi
In this work, we obtain the approximate solution for the integrodifferential equations by adding perturbation terms to the right hand side of integrodifferential equation and then solve the resulting equation using Chebyshev-Galerkin method. Details of the method are presented and some numerical results along with absolute errors are given to clarify the method. Where necessary, we made comparison with the results obtained previously in the literature. The results obtained reveal the accuracy of the method presented in this study.

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