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Permanent URI for this collectionhttps://repository.ui.edu.ng/handle/123456789/423
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Item Application of ordinal logistic regression model to occupation data(Duncan Science Company, 2009) Adepoju, A. A.; Adegbite, M.People's occupational choices might be influenced by their parents' occupation, gender, previous experiences, ages, and their own education level. We can study the relationship of one's occupation choice with education level and father's occupation. The occupational choices will be the outcome variable which consists of categories of occupations. The regression methods are capable of allowing researchers to identify explanatory variables related to organizational programs and services that contribute to the overall staff status. These methods also permit researchers to estimate the magnitude of the effect of the explanatory variables on the outcome variable. Therefore, regression methods seem to be superior in studying the relationship between the explanatory and outcome variables. This study used ordinal logistic regression method to examine the relationship between the ordinal outcome variable, different levels of staff status in the Lagos State Civil Service of Nigeria, the explanatory variables are Gender, Indigenous status, Educational Qualification, Previous Experience and Age. The outcome variable was measured on an ordered, categorical, and three-point Likert scale as Junior staff Middle Management staff, and Senior Management staff. Within the complete models, the legit link was the better choice because of its satisfying parallel lines assumption and larger model- fitting statistics. The study revealed that two explanatory variables namely, Education Qualification and Previous Working Experience significantly predicted the probability of an individual staff being a member of any of the three levels of staff statusItem Performances of the full information estimators in a two-equation structural model with correlated disturbances(Bachudo Science, 2009) Adepoju, A. A.The performances of two full information techniques, Three Stage Least Squares (3SLS) and Full Information Maximum Likelihood (FIML) of simultaneous equation models with correlated disturbance terms are compared with the Ordinary Least Squares (OLS) method in small samples. Comparative performance evaluation of the estimators was done using Average of Estimates, Total Absolute Bias (TAB) of Estimates, Root Mean Squared Error (RMSE) and Sum of Squared Residuals (RSS) of parameter estimates. The results of the Monte Carlo experiment showed that OLS is best with large negative or positive correlation, while 3SLS is best with feebly correlated error terms in the case of replication-based averages. The total absolute biases increase consistently as the sample size increases for OLS while FIML estimates reveal no distinct pattern. The magnitudes of the estimates yielded by two estimators, OLS and 3SLS, exhibited fairly consistent reaction to changes in magnitudes and direction of correlations of error termsItem GEOADDITIVE BAYESIAN MODEL FOR DATA WITH LIMITED SPATIAL INFORMATION(2015-04-21) OLUBIYI, A. O.Large area estimation has been mostly accomplished using Geoadditive Models (GM) which combines the ideas of Geostatistics and additive models. The GM relaxes the classical assumptions of traditional parametric model by simultaneously incorporating linear and nonlinear, nonparametric effects of covariates, nonlinear interactions and spatial e ects into a Geoadditive predictor. In the past, estimation of GM has been based on large area as a result of insu cient information in small areas. However, Bayesian approach allows out-of-sample information which can be used to augment the limited information in small areas. Hence, this study adopted the Geoadditive Bayesian model to estimate small areas with insu cient spatial information focusing on small district areas. The GM by Kamman and Wand was speci ed by using E ect Coding (EC) to capture the spatial e ect. The posterior was obtained by combining the likelihood (data) with the prior (out-of-sample) information. The likelihood and the prior information were assumed to be Gaussian and inverse gamma distribution respectively. The numerical solutions were obtained for the posterior distribution, which were not having a closed form solution, using Markov Chain Monte Carlo (MCMC) simulation technique. Finite di erence and partial derivative methods were used to estimate other components of the Geoadditive Bayesian model. Kane analyser was used to collect vehicular emission (carbondioxide, carbonmonoxide and hydrocarbon). Information were also collected on age of vehicles, vehicle types (car and buses), vehicle uses (private and commercial) from 9211 vehicles for 3 years (2008-2011) covering 4 locations: Abeokuta, Sagamu, Ijebu-Ode and Sango-Ota. Data were also collected on respiratory health records of 9211 individuals (18 years and below) in six di erent hospitals on number of visits (nv) and diagnosis within the locality of the collection point of pollutants. Exploratory DataAnalysis (EDA) was carried out on emitted pollutants and age of vehicles. Autocorrelation plot was used to determine model performance. The Geoadditive Bayesian model was : exp[g0(t) + 1 p2 2 e1 2 2 j p j=1zij + 1 p2 2 e 1 2 2 ( j )2 + 1 q2 2 j e1 22 ( spat)2 + 1 q2 2 j e1 22 ( gi)2 ]:exp Z 1 0 exp(g0(u) + p i=1gj(u)zij)du where zij ,gj , spat and j were non-linear time varying e ect, linear time varying e ect, spatial e ect, and random component, respectively. The MCMC simulation technique gave the posterior means and the standard errors. This revealed that nv, diagnosis, vehicle uses, vehicle types jointly determine the health e ect of pollutants on the individuals considered. Compared with Abeokuta individuals who lived in Sagamu (posterior mean = 0.036) were more likely to be a ected by emitted pollutants while those in Sango-Ota (posterior = - 0.002) and Ijebu-Ode (posterior = - 0.015) were less likely to be a ected. The EDA indicated non-linearity in the pollutants and age of vehicles. There were convergences of parameters at 250 Lag. A signi cant increase in the nonlinear e ects was observed for age of vehicle (5years - 12years), Carbondioxide (10100 - 14400 ppm), Carbonmonoxide (0 - 25000 ppm) and hydrocarbon (4953 - 19812 ppm). The derived Geoadditive Bayesian Model was found suitable and therefore recommended for estimating location e ect of small areas with limited spatial information