Statistics
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Item Ranking of simultaneous equation techniques to small sample properties and correlated random deviates(Science Publications, 2009) Adepoju, A.A; Olaomi, J.OProblem statement: All simultaneous equation estimation methods have some desirable asymptotic properties and these properties become effective in large samples. This study is relevant since samples available to researchers are mostly small in practice and are often plagued with the problem of mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The objective of this research was to study the small sample properties of these estimators when the errors are correlated to determine if the properties will still hold when available samples are relatively small and the errors were correlated. Approach: Most of the evidence on the small sample properties of the simultaneous equation estimators was studied from sampling (or Monte Carlo) experiments. It is important to rank estimators on the merit they have when applied to small samples. This study examined the performances of five simultaneous estimation techniques using some of the basic characteristics of the sampling distributions rather than their full description. The characteristics considered here are the mean, the total absolute bias and the root mean square error. Results: The result revealed that the ranking of the five estimators in respect of the Average Total Absolute Bias (ATAB) is invariant to the choice of the upper (P1) or lower (P2) triangular matrix. The result of the FIML using RMSE of estimates was outstandingly best in the open-ended intervals and outstandingly poor in the closed interval (- 0.05Item Ranking of simultaneous equation techniques to small sample properties and correlated random deviates(Science Publications, 2009) Adepoju, A.A.; Olaomi, J.O.Problem statement: All simultaneous equation estimation methods have some desirable asymptotic properties and these properties become effective in large samples. This study is relevant since samples available to researchers are mostly small in practice and are often plagued with the problem of mutual correlation between pairs of random deviates which is a violation of the assumption of mutual independence between pairs of such random deviates. The objective of this research was to study the small sample properties of these estimators when the errors are correlated to determine if the properties will still hold when available samples are relatively small and the errors were correlated. Approach: Most of the evidence on the small sample properties of the simultaneous equation estimators was studied from sampling (or Monte Carlo) experiments. It is important to rank estimators on the merit they have when applied to small samples. This study examined the performances of five simultaneous estimation techniques using some of the basic characteristics of the sampling distributions rather than their full description. The characteristics considered here are the mean, the total absolute bias and the root mean square error. Results: The result revealed that the ranking of the five estimators in respect of the Average Total Absolute Bias (ATAB) is invariant to the choice of the upper (P1) or lower (P2) triangular matrix. The result of the FIML using RMSE of estimates was outstandingly best in the open-ended intervals and outstandingly poor in the closed interval (- 0.05Item Efficiency in linear model with AR (1) and correlated error-regressor(African Research Review, 2009-04) Olutunji., O.J.; Adepoju., A.AIn this study, we conduct several Monte-Carlo experiments to examine the sensitivity of the estimators relative to OLS using the Variance and RMSE criteria, in the presence of first order autocorrelated error terms which are also correlated with geometric regressor. We examine the of efficiency to ı, _, as well as, its asymptotic behaviour, N, when the above two assumptions are violated. We observe that CORC and HILU give similar result, same for ML and MLGRID. OLS is more efficient than CORC and HILU while ML and MLGRID dominate OLS. In the scenarios, efficiency does increase with increase in autocorrelation level, only ML and MLGRID at _ = 0.05 show that efficiency increases with increase in autocorrelation level. All estimators show that efficiency reducesas significant level increases only when the autocorrelation value and samplesize are small (_ = 0.4, N = 20). There is more efficiency gain when N and _are large at all significant correlation levels. Asymptotically, the efficiency of FGLS estimators increase with increasing autocorrelation but it is in different to the correlation levels. The asymptotic ranking is CORC and HILU followed by MLGRID and ML.Item An alternative technique to ordinal logistic regression model under failed parallelism assumption(2009-12) Adeleke, K.A .; Adepoju, A.AMaternal health status is often measured in medical studies on an ordinal scale but data of this type arc generally reduced for analysis to a single dichotomy. Several statistical models have been developed to make full use of information in ordinal response data, but have not been much used in analyzing pregnancy outcomes. The authors discussed two of these statistical models the ordinal logistic regression model and the multinomial logistic regression model. Logistic regression models are used to analyze the dependent variable with multiple outcomes which can either be ranked or not. In this study, we described two logistic regression models for analyzing the categorical response variable. The first model uses the proportion-! odds model while the second uses the multinomial logistic regression model. The fits of these models using data on delivery from a Nigerian State hospital record/database were illustrated and compared to study the pregnancy outcomes. Analyses based on these models were carried out using STATA statistical package. The Multinomial logistic regression was found to be an important alternative to the ordinal regression technique when proportional odds assumption failed. The weight of the baby and the mother's history of disease (treated or not treated) were found to be important in determining the pregnancy outcome.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 A comparison of least squares dummy variable (LSDV) and the pooled estimator in fixed effect model(2008) Olayiwola, O. M.; Adepoju, A.A.; Olajide, J. TThis paper examines a comparison of Least Squares Dummy Variable and a pooled estimator in a fixed effect model. The aims of the research are: to estimate the individuals firms parameters by using least squares dummy variables. To estimate parameter of the pooled observations using ordinary least squares. To estimate the behavioral relationship between individuals variables and to test for the significance différence across the groups. The framework was based on a fixed effect model. The analysis of a panel model was carried out using the Ordinary Least Squares (OLS) and Least Squares Dummy Variable (LSDV) methods. various tests were carried out to determine which of the methods to use when dealing with a panel data. The results of the analysis showed significant différence across the different groups effect. F is also significant at 95% level by using either the fixed effect or pooled model in a panel data. Also, a measure of fit of the model carried out showed that the fixed effect model significantly explained the variation in the dependent variable while pooling the model explained a very small proportion of the total variation in the dépendent variable. In the light of the above, it will be appropriate to use a fixed effect least squares dummy variable rather than pooling the data in the analysis of a panel dataItem 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 The effect of students' pre-admission performance on post-admission performance(2008) Olayiwola, O. M.; Adepoju, A. A.; Okunlade, A.; Akomolafe, A.This study uses canonical correlation analysis to investigate the effect of pre-admission performance (performance in SSCE/GCE and JAMB) on the post-admission performance (performance in 1OOlevel to 400 Level). The study population comprised of a set of students that were admitted in the same year in the department of computer science. University of Ibadan, Nigeria. The students’ SSCE/GC, JAMB. 100 Level to 400 level results were studied. The result shows that students ' pre-admission performance are highly correlated with their 100 level to 300 level, but uncorrelated with 400 level result. This may due to: complexity of the course as they are moving higher, effect of the project work. Strike, riot, lack of relevant textbooks, social activities. etc. This study then recommends that the University should introduce or assign level advisers to advice the students; they should ensure that they provide relevant textbooks to the library for the students.Item Comparative performance of the limited information techniques in a two equation structural model(2007) Adepoju, A. A.The samples with which we deal in practice are rather small. seldom exceeding 80 observations and frequently much smaller. 'Thus, it is of great interest to inquire into the properties of estimators for the typical sample sizes encountered in practice. The performances of three simultaneous estimation method using a model consisting of a mixture of an identified and over identified equations with correlated error terms and compared. The result of the Monte Carlo study revealed that the Two Stage least Squares (2SLS) and the Limited Information Maximum Likelihood (LIML) estimates are similar and in most cases identical in respect of the just-identified equation. The Total Absolute Biases (TAB) of 2SLS and LIML revealed asymptotic behavior under (upper triangular matrix) P1 while those of Ordinary Least Squares (OLS) exhibited no such behavior. For both upper and lower triangular matrices (P, and P2), 2SI.S estimates showed asymptotic behavior in the middle interval. The OI.S is the only stable estimator with a stable behavior of Root Mean Square Error (RM3F.) as its estimates increase (decrease) consistently for equation 1(equation 2) for P, (for P2).Item Statistical theory and methods(2002) Shangodoyin, D. K.; Olubusoye, O. E.; Shittu, O. I.; Adepoju, A A.