Abstract: The aim of this study was to employ Maximum Likelihood (MLE) jointly with a numerical Method (Newton Raphson method) to obtain parameter estimates from the two-parameter Gamma model. The profile likelihood of the two-parameter Gamma model was also put into consideration. The methods were demonstrated using simulation studies and real life data considering data sets generated by R statistical software for different sample sizes. Standard errors were computed and 5 % Wald-confidence interval was constructed for the estimates of the model. The result of the study shows that Maximum Likelihood Estimation (MLE) jointly with Newton Raphson method was more efficient for estimating parameters of the Gamma model in simulation study than real life data. The study recommends that parameter estimates from the two-parameter Gamma model should be obtained by employing Maximum Likelihood Estimation jointly with Newton Raphson Method.Abstract: The aim of this study was to employ Maximum Likelihood (MLE) jointly with a numerical Method (Newton Raphson method) to obtain parameter estimates from the two-parameter Gamma model. The profile likelihood of the two-parameter Gamma model was also put into consideration. The methods were demonstrated using simulation studies and real life data cons...Show More
Abstract: This paper focuses on the Asymptotic Classification Procedures in Two Group Discriminate Analysis with Multivariate Binary Variables. Two data patterns were simulated using the R-Software Statistical Analysis System 2.15.3 and was subjected to two linear classification namely; Location and Logistic Models. To judge the performance of these models, the apparent error rates for each procedure are obtained for different sample sizes. The results obtained show that the location model performed better than Logistic Discrimination with the variation in the error rates being higher for Logistic Discrimination rule.Abstract: This paper focuses on the Asymptotic Classification Procedures in Two Group Discriminate Analysis with Multivariate Binary Variables. Two data patterns were simulated using the R-Software Statistical Analysis System 2.15.3 and was subjected to two linear classification namely; Location and Logistic Models. To judge the performance of these models, ...Show More
