Browsing by Author "Adebayo, P. O."
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Item An Alternative Solution to Hotelling T Square under the Heteroscedasticity of the Dispersion Matrix(Akamai University, U.S.A, 2019) Adebayo, P. O.; Oyeyemi, G. M.This work focuses on developing an alternative procedure to multivariate Behrens–Fisher problem by using approximate degree of freedom test which was adopted from Satterthwaite univariate procedure. The proposed procedure was compared via R package simulation and real-life data used by Timm with six (6) existing procedures namely: Johanson, Yao, Krishnamoorthy, Hotelling T square, Nel and Van der Merwe, and Yanagihara. It was discovered that the proposed procedure performed better in terms of power of the test, then all existing procedures considered in all the scenarios that include at different: (i) random variables (p), (ii) variance co–variance matrix, (iii) sample size, and (iv) significance level (α). The proposed procedure is completely favorably, well in terms of type I error rate with Johanson, Yao, Krishnamoorthy, Nel, and Van der MerweItem Comparison of the Invariant Solutions to the Multivariate Behren-Fisher Problems(Nigerian Association of Mathematical Physics, 2016) Oyeyemi, G. M.; Adebayo, P. O.Four of numerous solutions to multivariate Behrens-Fisher problem were selected for comparison. The four selected invariant solutions were; Yao, Johansen, James and Krishnamorrthy and Yu. Data were simulated to compare the four solutions under different distributions (Multivariate Beta, Multivariate Gamma and Multivariate Normal), sample sizes (N = 20, 30, 50, 100, 200, 400 and 600), number of variables (p = 2, 3 and 5) and for equal and unequal sample sizes. The comparisons were done at three levels of significance (alpha = 0.01, 0.025 and 0.05) using power of the test and type I error rate. The results showed that James procedure is better than all other procedures when p = 2 with small sample sizes, because it has second highest power with the lowest type I error rate. But when number of variables p = 3 or 4 and with large sample size, all the four procedures’ performances are the same.Item EVALUATION OF HOTELLING T2 AND MULTIVARIATE ANALYSIS OF VARIANCE TESTS(Faculty of Sciences, Federal University of Technology Minna, Nigeria, 2016) Oyeyemi, G. M.; Adebayo, P. O.; Folorunsho, A. I.Multivariate analysis of variance (MANOVA) which comprises of Wilks' lambda, Pillai's trace, Lawley-Hotelling trace and Roy's largest root are compared with Hotelling T square when null hypothesis is true. Data were simulated to compare the five (5) test statistics under the two different distributions (Multivariate Gamma and Multivariate Normal), sample size (10, 30, 60, 90, 400, 500, 800 and 1000), number of variables p = 2 and equal and unequal sample size and variance covariance matrix. The comparisons were done at two levels of significant (alpha = 0.01 and 0.05) using power of the test and type I error rate. The results showed that Roy's largest root test statistic is better than all other test statistics considered when sample size are equal but Hotelling T square performed better for unequal sample sizeItem Stand-in Procedure to Multivariate Behrens-Fisher Problem(Akamai University, U.S.A, 2018) OYEYEMI, G. M.; Adebayo, P. O.This work considers the problem of comparing two multivariate normal mean vectors under the heteroscedasticity of dispersion matrices. We develop a new procedure using approximate degree of freedom method by Satterthwaite [23] and broaden it to Multivariate Behrens-Fisher. The New procedure is compared with existing ones via R package simulation and Data used by James [8] and Yao [31]. We ascertain that, new procedure is better in terms of power of the test and type I error rate than all existing procedure mull over when the sample sizes are not equal, but the proposed procedure perform the same with the selected procedure when sample sizes are equal.