Oyebayo Ridwan Olaniran
@unilorin.edu.ng
Lecturer, Faculty of Physical Sciences
University of Ilorin
Oyebayo Ridwan OLANIRAN (PhD) is a lecturer in the Department of Statistics, Faculty of Physical Sciences, University of Ilorin. He has over nine years of university teaching, research, and administrative experience.
He has to his credit many publications in reputable outlets covering journals and edited conference proceedings. He has successfully supervised several undergraduate projects, postgraduate diploma and Master dissertations.
Dr. Olaniran is a member of professional bodies within and outside Nigeria, including the Nigeria Mathematical Society (NMS), International Society of Clinical Biostatistics (ISCB), International Biometrics Society-Group Nigeria (IBS-Gni), International Society for Bayesian Analysis (ISBA), and American Society for Clinical Oncology (ASCO).
EDUCATION
EDUCATION
Universiti Tun Hussein Onn, Malaysia, Batu Pahat, Johor Malaysia. Sept, 2016 – Oct, 2019
PhD Science, Statistics,
University of Ilorin, Ilorin, Kwara State, Nigeria. Oct, 2014 – April, 2016
Master of Science, Statistics, 79.63/100, Distinction.
University of Ilorin, Ilorin, Kwara State, Nigeria. Oct, 2009 – June, 2013
Bachelor of Science, Statistics, 4.82/5.00, First Class.
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics, Probability and Uncertainty, Statistics and Probability
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, and Abdulmajeed Atiah Alharbi
MDPI AG
Fractional cointegration in time series data has been explored by several authors, but panel data applications have been largely neglected. A previous study of ours discovered that the Chen and Hurvich fractional cointegration test for time series was fairly robust to a moderate degree of heterogeneity across sections of the six tests considered. Therefore, this paper advances a customized version of the Chen and Hurvich methodology to detect cointegrating connections in panels with unobserved fixed effects. Specifically, we develop a test statistic that accommodates variation in the long-term cointegrating vectors and fractional cointegration parameters across observational units. The behavior of our proposed test is examined through extensive Monte Carlo experiments under various data-generating processes and circumstances. The findings reveal that our modified test performs quite well comparatively and can successfully identify fractional cointegrating relationships in panels, even in the presence of idiosyncratic disturbances unique to each cross-sectional unit. Furthermore, the proposed modified test procedure established the presence of long-run equilibrium between the exchange rate and labor wage of 36 countries’ agricultural markets.
Oyebayo Ridwan Olaniran, Ali Rashash R. Alzahrani, and Mohammed R. Alzahrani
MDPI AG
This paper examines the distribution of eigenvalues for a 2×2 random confusion matrix used in machine learning evaluation. We also analyze the distributions of the matrix’s trace and the difference between the traces of random confusion matrices. Furthermore, we demonstrate how these distributions can be applied to calculate the superiority probability of machine learning models. By way of example, we use the superiority probability to compare the accuracy of four disease outcomes machine learning prediction tasks.
Saidat Fehintola Olaniran, Oyebayo Ridwan Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, and Mohd Tahir Ismail
MDPI AG
Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests having been developed. However, most previously developed testing procedures for fractional cointegration are primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test’s development is based on a bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.
Oyebayo Ridwan Olaniran and Ali Rashash R. Alzahrani
MDPI AG
Random forest (RF) is a widely used data prediction and variable selection technique. However, the variable selection aspect of RF can become unreliable when there are more irrelevant variables than relevant ones. In response, we introduced the Bayesian random forest (BRF) method, specifically designed for high-dimensional datasets with a sparse covariate structure. Our research demonstrates that BRF possesses the oracle property, which means it achieves strong selection consistency without compromising the efficiency or bias.
Oyebayo Ridwan Olaniran and Mohd Asrul A. Abdullah
Elsevier BV
Jibril Abubakar, Mohd Asrul Affendi Abdullah, and Oyebayo Ridwan Olaniran
International Academic Press
The exponential, Weibull, log-logistic and lognormal distributions represent the class of light and heavy-tailed distributions that are often used in modelling time-to-event data. The exponential distribution is often applied if the hazard is constant, while the log-logistic and lognormal distributions are mainly used for modelling unimodal hazard functions. The Weibull distribution is on the other hand well-known for modelling monotonic hazard rates. Recently, in practice, survival data often exhibit both monotone and non-monotone hazards. This gap has necessitated the introduction of Exponentiated Weibull Distribution (EWD) that can accommodate both monotonic and non-monotonic hazard functions. It also has the strength of adapting unimodal functions with bathtub shape. Estimating the parameter of EWD distribution poses another problem as the flexibility calls for the introduction of an additional parameter. Parameter estimation using the maximum likelihood approach has no closed-form solution, and thus, approximation techniques such as Newton-Raphson is often used. Therefore, in this paper, we introduce another estimation technique called Variational Bayesian (VB) approach. We considered the case of the accelerated failure time (AFT) regression model with covariates. The AFT model was developed using two comparative studies based on real-life and simulated data sets. The results from the experiments reveal that the Variational Bayesian (VB) approach is better than the competing Metropolis-Hasting Algorithm and the reference maximum likelihood estimates.
M. B. Mohammed, H. S. Zulkafli, N. Ali, O. R. Olaniran, and H. Ahmed
Informa UK Limited
Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, and Jumoke Popoola
Springer International Publishing
Oyebayo Ridwan Olaniran
AIP Publishing
O R Olaniran and M A A Abdullah
IOP Publishing
Abstract In this study, the Variational Bayes (VB) approach was hybridized with the bootstrap prior procedure to improve the accuracy of subset selection as well as optimizing the algorithm time in modelling high-dimensional genomic data with inherent sparse structure. The new hybrid VB approach is shown to yields a minimal sufficient statistic which under mild regularity conditions converges to the true sparse structure. Simulation and real-life high-dimensional genomic data experiments revealed comparable empirical performance with other competing frequentist and Bayesian methods. In addition, a new fast algorithm that illustrates the procedure was developed and implemented in the environment of R statistical software as package “VBbootprior”.
Jumoke Popoola, Waheed Babatunde Yahya, Olusogo Popoola, and Oyebayo Ridwan Olaniran
International Academic Press
Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.
Oyebayo Ridwan Olaniran and Mohd Asrul Affendi Abdullah
Austrian Statistical Society
In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.
Oyebayo Ridwan Olaniran and Mohd Asrul Affendi Abdullah
Wayne State University Library System
A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.
Siti Afiqah Muhamad Jamil, M. Asrul Affendi Abdullah, Sie Long Kek, Oyebayo Ridwan Olaniran, and Syahila Enera Amran
IOP Publishing
In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.
Oyebayo Ridwan Olaniran and Waheed Babatunde Yahya
Wayne State University Library System