Waheed Babatunde Yahya
@unilorin.edu.ng
Professor, Faculty of Physical Sciences
Professor, Faculty of Physical Sciences
University of Ilorin, Ilorin, Nigeria
RESEARCH, TEACHING, or OTHER INTERESTS
Statistics and Probability, Statistics, Probability and Uncertainty, Management Science and Operations Research
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Paul Olujide Adebayo, Rasheed Gbenga Jimoh, and Waheed Babatunde Yahya
Elsevier BV
Waheed Babatunde Yahya and Muhammad Adamu Umar
Springer Science and Business Media LLC
Kazeem A. Dauda, Kabir O. Olorede, Alabi W. Banjoko, Waheed B. Yahya, and Yusuf O. Ayipo
CRC Press
Morolake Oladayo Lawrence, Rasheed Gbenga Jimoh, and Waheed Babatunde Yahya
Springer Science and Business Media LLC
Yusuf Bello, Waheed B. Yahya, and Abdulrazaq AbdulRaheem
Nigerian Society of Physical Sciences
In clustered data, observations within a cluster show similarity between themselves because they share common features different from observations in the other clusters. In a given population, different clustering may surface because correlation may occur across more than one dimension. The existing multilevel analysis techniques of the primal linear mixed-effect models are limited to natural clusters which are often not realistic to capture in real-life situations. Therefore, this paper proposes dual linear mixed models (DLMMs) for modeling unobserved latent clusters when such are present in data sets to yield appreciable gains in model fitness and predictive accuracy. The methodology explored the development and analysis of the dual linear mixed models (DLMMs) based on the derived latent clusters from the natural clusters using multivariate cluster analysis. A published data set on political analysis was used to demonstrate the efficiency of the proposed models. The proposed DLMMs have yielded minimum values of the models' assessment criteria (Akaike information criterion, Bayesian information criterion, and root mean squared error), and hence, outperformed the classical PLMMs in terms of model fitness and predictive accuracy.
Chinenye Pauline Ezenweke, Isaac Adeola Adeniyi, Waheed Babatunde Yahya, and Rhoda Enemona Onoja
Elsevier BV
Lateef Babatunde Amusa, Waheed Babatunde Yahya, and Annah Vimbai Bengesai
Public Library of Science (PLoS)
Childhood undernutrition is a major public health challenge in sub-Saharan Africa, particularly Nigeria. Determinants of child malnutrition may have substantial spatial heterogeneity. Failure to account for these small area spatial variations may cause child malnutrition intervention programs and policies to exclude some sub-populations and reduce the effectiveness of such interventions. This study uses the Composite Index of Anthropometric Failure (CIAF) and a geo-additive regression model to investigate Nigeria’s prevalence and risk factors of childhood undernutrition. The geo-additive model permits a flexible, joint estimation of linear, non-linear, and spatial effects of some risk factors on the nutritional status of under-five children in Nigeria. We draw on data from the most recent Nigeria Demographic and Health Survey (2018). While the socioeconomic and environmental determinants generally support literature findings, distinct spatial patterns were observed. In particular, we found CIAF hotspots in the northwestern and northeastern districts. Some child-related factors (Male gender: OR = 1.315; 95% Credible Interval (CrI): 1.205, 1.437) and having diarrhoea: OR = 1.256; 95% CrI: 1.098, 1.431) were associated with higher odds of CIAF. Regarding household and maternal characteristics, media exposure was associated with lower odds of CIAF (OR = 0.858; 95% CrI: 0.777, 0.946). Obese maternal BMI was associated with lower odds of CIAF (OR = 0.691; 95% CrI: 0.621, 0.772), whereas, mothers classified as thin were associated with higher odds of CIAF (OR = 1.216; 95% CrI: 1.055, 1.411). Anthropometric failure is highly prevalent in Nigeria and spatially distributed. Therefore, localised interventions that aim to improve the nutritional status of under-five children should be considered to avoid the under-coverage of the regions that deserve more attention.
Ezekiel Adebayo Ogundepo and Waheed Babatunde Yahya
Springer Science and Business Media LLC
Adeyinka Solomon Ogunsanya, Waheed Babatunde Yahya, Taiwo Mobolaji Adegoke, Christiana Iluno, Oluwaseun R. Aderele, and Matthew Iwada Ekum
Horizon Research Publishing Co., Ltd.
In this work, a three-parameter Weibull Inverse Rayleigh (WIR) distribution is proposed. The new WIR distribution is an extension of a one-parameter Inverse Rayleigh distribution that incorporated a transformation of the Weibull distribution and Log-logistic as quantile function. The statistical properties such as quantile function, order statistic, monotone likelihood ratio property, hazard, reverse hazard functions, moments, skewness, kurtosis, and linear representation of the new proposed distribution were studied theoretically. The maximum likelihood estimators cannot be derived in an explicit form. So we employed the iterative procedure called Newton Raphson method to obtain the maximum likelihood estimators. The Bayes estimators for the scale and shape parameters for the WIR distribution under squared error, Linex, and Entropy loss functions are provided. The Bayes estimators cannot be obtained explicitly. Hence we adopted a numerical approximation method known as Lindley's approximation in other to obtain the Bayes estimators. Simulation procedures were adopted to see the effectiveness of different estimators. The applications of the new WIR distribution were demonstrated on three real-life data sets. Further results showed that the new WIR distribution performed credibly well when compared with five of the related existing skewed distributions. It was observed that the Bayesian estimates derived performs better than the classical method.
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.
A W Banjoko, W B Yahya, M K Garba, and K O Abdulazeez
IOP Publishing
Abstract This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.
Anthony Ekpo and Waheed Babatunde Yahya
Knowledge E
Background and aim: In this paper, we present results regarding the outcomes of some anthropometric, epidemiological and demographic factors on the nutritional status of the under-five children which were categorized into three ordinal groups of Severe Acute Malnutrition (SAM), Moderate Acute Malnutrition (MAM) and Global Acute Malnutrition (GAM) in Kazaure Local Government Area in Nigeria.
 Methods: An ordinal logistic model that depicted the log-odds in favour of GAM (normal) child was fitted to the data based on surveillance indexed by Weight-For-Height (WFH).
 Results:The results showed that the proportional odd of measuring the nutritional status of a child in a nutrition survey using the WFH index has the OR= 7.43 (95% CI, 4.717 to 11.705) times greater, with Wald (1) 2 =74.81, p<0.001, hence a statistically significant effect.
 Conclusion: Based on the results and summary of findings, it can be concluded that age is a major predictor of the nutrition status of a child in a nutritional study when the surveillance is based on WFH index unlike sex and measles that do not play a major role.
B. T. Babalola and W. B. Yahya
Knowledge E
Background: The Cox proportional hazard model has gained ground in Biostatistics and other related fields. It has been extended to capture different scenarios, part of which are violation of the proportionality of the hazards, presence of time dependent covariates and also time dependent co-efficients. This paper focuses on the behaviour of the Cox Model in relation to time coefficients in the presence of different levels of collinearity.
 Objectives: The objectives of this study are to examine the effects of collinearity on the estimates of time dependent co-effiecients in Cox proportional hazard model and to compare the estimates of the model for the logarithm and the square functions of time.
 Materials and methods: The Algorithm based on a binomial model was extended in order to incorporate the different correlation structures required for the study. The scaled Schoenfeld residuals plots revealed the behaviour of the estimated betas at different degrees of collinearity. Results and conclusions are based of outcome of simulation study performed only.
 Results: The estimated betas were compared to the true betas at the different level of collinearity in graphical pattern.
 Conclusion: The study shows that collinearity is a huge factor that influences the correctness of the estimates of the regressors within the framework of Cox model.
A. W. Banjoko, W. B. Yahya, and M. K. Garba
Knowledge E
Background & Aim: In this study, efficient Support Vector Machine (SVM) algorithm for feature selection and classification of multi-category tumour classes of biological samples using gene expression profiles was proposed.
 Methods: Feature selection interface of the algorithm employed the F-statistic of the ANOVA–like testing scheme at some chosen family-wise-error-rate which ensured efficient detection of false-positive genes. The selected gene subsets using the above method were further screened for optimality using the Misclassification Error Rates yielded by each of them and their combinations in a sequential selection manner. In a 10-fold cross-validation, the optimal values of the SVM parameters with appropriate kernel were determined for tissue sample classification using one-versus-all approach. The entire data matrix was randomly partitioned into 95% training set to train the SVM classifier and 5% test set to evaluate the predictive performance of the classifier over 1,000 Monte-Carlo cross-validation runs. Published microarray breast cancer dataset with five clinical endpoints was employed to validate the results from the simulation studies.
 Results: Results from Monte-Carlo study showed excellent performance of the SVM classifier with higher prediction accuracy of the tissue samples based on the few gene biomarkers selected by the proposed feature selection method.
 Conclusion: SVM could be considered as a classification of multi-category tumour classes of biological
Oyebayo Ridwan Olaniran and Waheed Babatunde Yahya
Wayne State University Library System
Samson Babatunde Adebayo and Waheed Babatunde Yahya
Springer Netherlands
W. B. Yahya and J. B. Olaifa
In this study, the techniques of ridge regression model as alternative to the classical ordinary least square (OLS) method in the presence of correlated predictors were investigated. One of the basic steps for fitting efficient ridge regression models require that the predictor variables be scaled to unit lengths or to have zero means and unit standard deviations prior to parameters’ estimations. This was meant to achieve stable and efficient estimates of the parameters in the presence of multicollinearity in the data. However, despite the benefits of this variable transformation on ridge estimators, many published works on ridge regression practically ignored it in their parameters’ estimations. This work therefore examined the impacts of scaled collinear predictor variables on ridge regression estimators. Various results from simulation studies underscored the practical importance of scaling the predictor variables while fitting ridge regression models. A real life data set on import activities in the French economy was employed to validate the results from the simulation studies.