Scholarly works http://ir.library.ui.edu.ng/handle/123456789/422 2025-09-27T08:21:42Z 2025-09-27T08:21:42Z Adepoju, A. A. Afolabi, K. http://ir.library.ui.edu.ng/handle/123456789/7716 2022-11-28T09:47:11Z 2018-04-01T00:00:00Z

Title: Using generalized estimating equation (gee) to analyse the influence of some factors on the state of health of diabetes patients Authors: Adepoju, A. A.; Afolabi, K. Abstract: In longitudinal studies, observations measured repeatedly from the same subject over time are serially correlated. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. Generalized Estimating Equation (GEE) is a general statistical approach to fit a marginal model for longitudinal/clustered data analysis, and it has been popularly applied into clinical trials and biomedical studies. Generalized linear Model (GLM)on the other hand has been widely used in fitting a regression to a set of data of dependent variables depending solely on a/some set of covariates with the different set of distributions and their link function and its use has been extended to longitudinal data. This paper examines the effects of some factors; age, sex, Body Mass Index (BMI), blood pressure, exercise and glucose tolerance on the health status of 840 diabetes patients attending clinic over a period of five years using the generalized linear model and the generalized estimating equations methods. The GEE performs better than the GLM. The result reveals that glucose tolerance, blood pressure and BMI are the important factors that affect the state of health of these patients

2018-04-01T00:00:00Z Adepoju, A. A . Ogundunmade, T.P . Adebayo, K. B. http://ir.library.ui.edu.ng/handle/123456789/7715 2022-11-28T09:30:42Z 2017-12-01T00:00:00Z

Title: Regression methods in the presence of heteroscedasticity and outliers Authors: Adepoju, A. A .; Ogundunmade, T.P .; Adebayo, K. B. Abstract: It has been observed over the years that real life data are usually non-conforming to the classical linear regression assumptions. One of the stringent assumptions that is unlikely to hold in many applied settings is that of homoscedasticity. When homogenous variance in a normal regression model is not appropriate, invalid standard inference procedure may result from the improper estimation of standard error when the disturbance process in a regression model present heteroscedasticity. When both outliers and heteroscedasticity exist, the inflation of the scale estimate can deteriorate. This study identifies outliers under heteroscedastic errors and seeks to study the performance of four methods; ordinary least squares (OLS), weighted least squares (WLS), robust weighted least squares (RWLS) and logarithmic transformation (Log Transform) methods to estimate the parameters of the regression model in the presence of heteroscedasticity and outliers. Real life data obtained from the Central Bank of Nigeria Bulletin and Monte Carlo simulation were carried out to investigate the performances of these four estimators. The results obtained show that the transformed logarithmic model proved to be the best estimator with minimum standard error followed by the robust weighted least squares. The performance of OLS is the least in this order

2017-12-01T00:00:00Z Awe, O. O. Sanusi, K.A. Adepoju, A. A. http://ir.library.ui.edu.ng/handle/123456789/7714 2022-11-28T09:30:27Z 2017-01-01T00:00:00Z

Title: An Application of Bayesian Dynamic Linear Model to Okun’s Law Authors: Awe, O. O.; Sanusi, K.A.; Adepoju, A. A. Abstract: Many authors have used dynamic time series regression models to analyse Okun’s law. This type of models often require first differencing the dependent and independent variables, as well as investigating the maximum lag length required for the model to be efficient. In this paper, we propose a straight-forward time-varying parameter state space model for analyzing Okun’s law. In particular, as a case study, we investigate the validity and stability of Okuns law using a Bayesian Dynamic Linear Model which implicitly describes the time-varying relationship between Gross Domestic Product (GDP) and unemployment rate of a major economy in Africa for three decades. The time-varying parameters of this model are estimated via a modified recursive forward filtering, backward sampling algorithm. We find that Okuns law exhibited structural instability in Nigeria in the period 1970-2011, with the sensitivity of unemployment rate to movements in output growth loosing stability over time, which may have been a contributor to her recent economic decline

2017-01-01T00:00:00Z Adepoju, A. A. Adebajo, E. O Ogundunmade, P. T. http://ir.library.ui.edu.ng/handle/123456789/7713 2022-11-28T09:13:10Z 2017-01-01T00:00:00Z

Title: Frequentist and bayesian estimation of parameters of linear regression model with correlated explanatory variables Authors: Adepoju, A. A.; Adebajo, E. O; Ogundunmade, P. T. Abstract: This paper addressed the popular issue of collinearity among explanatory variables in the context of a multiple linear regression analysis, and the parameter estimations of both the classical and the Bayesian methods. Five sample sizes: 10, 25, 50, 100 and 500 each replicated 10,000 times were simulated using Monte Carlo method. Four levels of correlation p = 0.0,0.1,0.5, and 0.9 representing no correlation, weak correlation, moderate correlation and strong correlation were considered. The estimation techniques considered were; Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS) and Bayesian Methods. The performances of the estimators were evaluated using Absolute Bias (ABIAS) and Mean Square Error (MSE) of the estimates. In all cases considered, the Bayesian estimators had the best performance. It was consistently most efficient than the other estimators, namely OLS and FGLS

2017-01-01T00:00:00Z