DSpace Angular :: Browsing by Author "Saidat Fehintola Olaniran"

Browsing by Author "Saidat Fehintola Olaniran"

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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 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
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.
Bayesian Additive Regression Trees for Predicting Colon Cancer: Methodological Study (Validity Study).
(Turkiye klinikleri, 2022-05-01) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran; Jumoke Popoola; Omekam Ifeyinwa Vivian
The occurrence of colon cancer starts in the inner wall of the large intestine. The survival of colon cancer patients strongly relies on early detection. Diagnosing colon cancer using clinical approaches often takes longer, especially in most developing countries with limited facilities. The recent use of microarray technology has presented a new approach for the oncologist to diagnose cancer cells using non-clinical machine learning methods. In this paper, the aim is to predict the status of colon cancer tissues using the Bayesian Additive Regression Trees (BART) and 2 other machine learning methods. Material and Methods: The development and comparative analysis of BART alongside 2 other competing methods (Random Forest: RF and Gradient Boosting Machine: GBM) were implemented. The dataset used for the analysis is the microarray colon cancer data which consists of 2,000 gene expression …
Bayesian Regularized Neural Network for Forecasting Naira-USD Exchange Rate
(Springer, 2022-05-04) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran; Jumoke Popoola
The Artificial Neural Network Autoregressive model (ANN-AR) is a recently adopted approach for forecasting Naira to the USD exchange rate. The Bayesian Regularized Neural Network (BRNN) is an alternative to ANN-AR based on the probabilistic interpretation of network weights. It is useful for solving overfitting problems inherent in ANN when large historical data are not available. In this paper, we developed a BRNN for modelling the monthly time series data of Naira to USD for four years. Performance analysis was observed using actual exchange rate values for the Year 2017 against the model predicted outcomes based on four evaluation metrics. Results from the analysis established the appropriateness of a BRNN for modelling short-term exchange rates in Nigeria.
Empirical bayesian binary classification forests using bootstrap prior
(Science Publishing Corporation-uthm.edu.my, 2018-11-30) Oyebayo Ridwan Olaniran; Mohd Asrul Affendi; Gopal Pillay, Khuneswar; Saidat Fehintola Olaniran
In this paper, we present a new method called Empirical Bayesian Random Forest (EBRF) for binary classification problem. The prior ingredient for the method was obtained using the bootstrap prior technique. EBRF addresses explicitly low accuracy problem in Random Forest (RF) classifier when the number of relevant input variables is relatively lower compared to the total number of input variables. The improvement was achieved by replacing the arbitrary subsample variable size with empirical Bayesian estimate. An illustration of the proposed, and existing methods was performed using five high-dimensional microarray datasets that emanated from colon, breast, lymphoma and Central Nervous System (CNS) cancer tumors. Results from the data analysis revealed that EBRF provides reasonably higher accuracy, sensitivity, specificity and Area Under Receiver Operating Characteristics Curve (AUC) than RF in most of the datasets used.
IMPROVED BAYESIAN FEATURE SELECTION AND CLASSIFICATION METHODS USING BOOTSTRAP PRIOR TECHNIQUES
(Faculty of Computer and Applied Computer Science, Tibiscus University of Timisoara, Romania, 2016) Oyebayo Ridwan Olaniran; Saidat Fehintola Olaniran; Yahya, Waheed Babatunde; Banjoko, Alabi; Garba, Mohammed Kabir; Amusa, Lateef; Gatta F.N
In this paper, the behavior of feature selection algorithms using the traditional t-test, Bayesian t-test using MCMC and Bayesian two sample test using proposed bootstrap prior technique were determined. In addition, we considered some frequentist classification methods like k- Nearest Neighbor (k-NN), Logistic Discriminant (LD), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Naïve Bayes when conditional independence assumption is violated. Two new Bayesian classifiers (B-LDA and B-QDA) were developed within the frame work of LDA and QDA using the bootstrap prior technique. The model parameters were estimated using Bayesian approach via the posterior distribution that involves normalizing the prior for the attributes and the likelihood from the sample in a Monte Carlo experiment. The bootstrap prior technique was incorporated into the Normal-Inverse-Wishart natural conjugate prior for the parameters of the multivariate normal distribution where the scale and location parameters were required. All the classifiers were implemented on the simulated data at 90:10 training-test data ratio. The efficiencies of these classifiers were assessed using the misclassification error rate, sensitivity, specificity, positive predictive value, negative predictive value and area under the ROC curve. Results from various analyses established the supremacy of the proposed Bayes classifiers (B-LDA and B-QDA) over the existing frequentists and Naïve Bayes classification methods considered. All these methods including the proposed one were implemented on a published binary response microarray data set to validate the results from the simulation study.
Laplace Approximation to the Posterior of Bayesian Weibull Model with Time-Varying Effect
(ijmaa, 2019) Saidat Fehintola Olaniran
n this paper, the estimation of the parameter of the Bayesian Weibull model with time-varying effect is considered. The Laplace approximation was developed and compared to the maximum likelihood approach achieved via the Newton-Raphson procedure. The efficiency of the two methods was analyzed using Monte-Carlo samples drawn from the time-varying Weibull model.
Modeling Short Run Relationship between Naira-USD Exchange Rate and Crude Oil Price in Nigeria
(Scretech, 2019-03) Saidat Fehintola Olaniran
The vector auto-regressive (VAR) model is one of the most successful, flexible, and easy to use models for the analysis of multivariate time series. It is a natural extension of the univariate auto-regressive model to a dynamic multivariate time series. The VAR model has proven to be especially useful for describing the dynamic behavior of economic and financial time series and for forecasting. It often provides superior forecasts to those from univariate time series models. The data used are monthly observations from January 2006 to October 2016 of Nigeria Crude Oil price and Naira to the dollar exchange rate. The VAR model was employed for modelling the data. The unit root test reveals that all the series are non-stationary at the level and stationary at first difference. The co-integration relations among the series indices were identified by applying Johansen’s cointegration test. The result of Johansen’s test indicates no existence of co-integration relation between the variables. The final result shows that a vector autoregressive (VAR) model of lag three with no co-integration equations best fits the data.
Purchasing power parity: The West African experience
(AIP Publisher, 2021-11-18) Saidat Fehintola Olaniran; Mohd Tahir Ismail
The fifteen heads of States of the Economic Community of the West African States (ECOWAS) are currently considering a unified currency called “Eco”. This decision suggests the need to study the purchasing power parity (PPP) of the existing currency for the various countries. Thus, in this paper, we investigate the existence of the relative PPP theory over a 50 years period that spanned through 1970 to 2019 for the sixteen West African Countries against the U.S. Dollars using a panel data analysis approach. The two approaches for testing the PPP hypothesis namely; the panel unit root test and the panel cointegration test were employed to check the existence of unit root and or cointegration between the nominal exchange rate of West African countries and price ratio. The results of Im-Pesaran-Shin panel unit root test show that the nominal exchange rate and price ratio panels have a unit root, which implies that the two variables are non-stationary in their level difference and thus they are I(1). On the other hand, the results from the Kao and Westerlund panel cointegration tests supported the existence of a long-run relationship between nominal exchange rate and price ratio. The convergence of the two results shows that there exists a long-run relationship between the two variables as well as a long-run PPP for all the sixteen West African countries. The results of this paper further imply that there is a long-run relationship between relative change in the exchange rate and price ratio for the West African countries over the period reviewed. Therefore, the recent decision to have a unified currency among the ECOWAS countries will yield a meaningful return in the long-run.
Testing Absolute Purchasing Power Parity in West Africa Using Fractional Cointegration Panel Approach
(Elsevier, 2023-03-02) Saidat Fehintola Olaniran; Mohd Tahir Ismail
One of the most effective ideas for comparing the values of two or more currencies is the purchasing power parity hypothesis (PPP). Most earlier works on PPP focused on using the standard cointegration approach by assuming a unit root for the observed series. This assumption is not always valid, especially in series with short-term dynamics. Thus, in this paper, we developed the fractional cointegrated panel approach for testing the absolute Purchasing Power Parity (PPP) model. The empirical illustration was achieved using the exchange rates and price ratios for the 16 West African countries for 52 years (1970 - 2021). The results from the fractional cointegration test confirm the presence of relative PPP for the panel of countries in the long run, while the estimation of long-run intercepts and cointegration vector confirms the absence of the absolute PPP for the panel of countries.

Browsing by Author "Saidat Fehintola Olaniran"

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