Browsing by Author "Akanbi, O. B."

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Application of regression type estimator in double sampling skills to students’ enrollment in Oyo State
(2019-03) Udomboso, C. G.; Akanbi, O. B.; Afolabi, S. A.
This research derived the precision using Regression Estimation technique with the application of secondary data obtained using the number of students enrollment in 2015 (Auxiliary variable “x”) and 2016 (response variable “y”) respectively in secondary schools of Ibadan, Oyo State, Nigeria for the purpose of obtaining average enrollment figures in the selected state in order to know the bright future of secondary schools in Oyo State in general and to establish the empirical comparison of the optimum variances in obtaining the most efficient estimator in order to satisfy the condition; p2≥ 122124 based on the coefficients of Variation for the validity and reliability, the relative efficiency was also determined based on the conditions attached to the supremacy in terms of the estimated mean square error (variance) whereby the regression line does not pass through the origin from the graph of Relative Efficiency (R.E) against Correlation Coefficients (p) that maintain inverse relation. Proper conclusions and recommendations are made based on findings from the analysis in terms of adequate record keeping among the contemporary states within.
Bayesian approach to survival modeling of remission duration for acute leukemia
(2019) Akanbi, O. B.; Oladoja, O. M.; Udomboso, C. G.
The problem of analyzing time to event data arises in a number of applied fields like biology and medicine. This study constructs a survival model of remission duration from a clinical trial data using Bayesian approach. Two covariates; drug and remission status, were used to describe the variation in the remission duration using the Weibull proportional hazards model which forms the likelihood function of the regression vector. Using a uniform prior, the summary of the posterior distribution; Weibull regression model of four parameters ( η, µ,β1, β2, was obtained. With Laplace transform, initial estimates of the location and spread of the posterior density of the parameters were obtained. In this present study, data from children with acute leukemia was used. The information from the Laplace transform was used to find a density for the Metropolis random walk algorithm from Markov Chain Monte Carlos simulation to indicate the acceptance rate (24.55%).