Browsing by Author "Salau, I. S."
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Item Comparison between Fisherian and Bayesian Approach to Classification Using two Groups(College of Natural and Applied Science, University of Porthacourt, Nigeria, 2014) Oyeyemi, G. M.; Oyebanji, L. A.; Salau, I. S.; Folorunsho, A. ITwo approaches to discriminant analysis procedure are examined and compared based on their misclassification error rate. The Fisher's approach tends to find a linear combination of the variables which maximize the ratio of the between group sum of squares to that of the within group sum of squares in achieving a good separation. On the other hand, the Bayesian approach assigns an observed unit to a group with the greatest posterior probability. Fisher's linear discriminant analysis though is the most widely used method of classification because of its simplicity, and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate.Item J2 Optimality and Multi-level Minimum Aberration Criteria in Fractional Factorial Design(International Institute for Science, Technology & Education., 2012) Salau, I. S.; Adeleke, B. L.; Oyeyemi, G. M.The desirable properties of fractional factorial design: Balance and orthogonal; was examined for near balance and near orthogonal using the balance coefficient and J2 optimality criteria respectively. Efficient orthogonal arrays with three factors having two, three and four levels were constructed with balance and orthogonal property for lowest common multiples of runs. The two forms of balance coefficient were used for classifying the designs into two and multi level minimum aberration criteria were used to determine designs with lesser aberration. It was observed that designs constructed using the maximum form of balance coefficient has the lesser aberration in both the generalized minimum aberration and minimum moment aberration criteria. The J2 – optimality criterion reveals that the higher the run of a design, the lesser it’s optimality value.Item On discrimination procedure with mixtures of continuous and categorical variables(Taylor and Francis, 2016) Oyeyemi, G. M.; Mbaeyi, G. C.; Salau, I. S.; Muse, B. O.A discrimination procedure, based on the location model is described and suggested for use in situation where the discriminating variables are mixtures of continuous and binary variables. Some procedures that have been previously employed, in a similar situation, like Fisher’s linear discriminant function and the logistic regression were compared with this method using error rate (ER). Optimal ERs for these procedures are reported using real and simulated data for the case of varying sample size and number of continuous and binary variables and were used as a measure for assessing the performance of the various procedures. The suggested procedure performed considerably better in the cases considered and never did produce a result that is poor when compared with other procedures. Hence, the suggested procedure might be considered for such situations.Item On The Estimation of Power and Sample Size in Test of Independence(Asian Network for Scientific Information, Pakistan, 2010) OYEYEMI, G. M.; Adewara, A. A.; Adebola, F. B.; Salau, I. S.In this study, power and sample size estimations in the context of test of independent between categorical variables were examined. The required sample size in an experiment is a function of the alternative hypothesis, the size of type I error and the variability of the population. Power of a test is the probability of rejecting the a false null hypothesis and it depends on the effect size, type I error and sample size. A priori power analysis is determination of minimum sample size to obtain a required power while post-hoc power analysis is calculating power of a test. A test with small effect size requires large sample size to achieve a power of 80% or more while effect size of medium or large size needs small sample size to achieve that. Test with small degrees of freedom will attain higher power than the same test with larger degrees of freedom.Item Review of Classical Methods in Supersaturated Designs (SSD) for Factor Screening(The International Institute for Science, Technology and Education (IISTE), 2015) Salau, I. S.; Adeleke, B. L.; Oyeyemi, G. M.Supersaturated designs are fractional factorial designs that have too few runs to allow the estimation of the main effects of all the factors in the experiment. There has been a great deal of interest in the development of these designs for factor screening in recent years. A review of supersaturated design is presented, including criteria for design selection, with reference to the popular E(s2) criterion and classical methods for constructing supersaturated designs. Classical methods have been suggested for the analysis of data from supersaturated designs and these are critically reviewed and illustrated.