Tag Archives: maximum likelihood

Application of Newton Raphson Method to Non – Linear Models (Published)

Maximum likelihood estimation is a popular parameter estimation procedure however parameters may not be estimable in closed form. In this work, the maximum likelihood estimates from different distributions were obtained after the failure of the likelihood approach. The targeted models are Non Linear models with an application to a Logistic regression model. Although, obtaining the estimate of parameters for non linear models cannot be easily obtained directly. That is the solution is intractable. So there is a need to look else where, so as to obtain the solutions . In this work, R statistical package was used in performing the analysis. The result shows that convergence was attained at the 18th iteration out of 21. This also provides the values and the maximum estimate for β0 and β1.

Keywords: Intractable Functions, Likelihood Function, maximum likelihood


This paper is a study of two-group classification models for binary variables. Eight classification procedures for binary variables are discussed and evaluated at each of 118 configurations of the sampling experiments. The results obtained ranked the procedures as follows: Optimal, Linear discriminant, Maximum likelihood, Predictive, Dillon Goldstein, Full multinomial, Likelihood and Nearest neighbour. Also the result of the study show that increase in the number of variables improve the accuracy of the models.

Keywords: Binary Variables, Classification Models, Dillon Goldstein, Linear Discriminant, Misclassification, Optimal, Predictive, and Multinomial, maximum likelihood


The use of classification rules for binary variables are discussed and evaluated. R-software procedures for discriminant analysis are introduced and analyzed for their use with discrete data. Methods based on the full multinomial, optimal, maximum likelihood rule and nearest neighbour procedures are treated. The results obtained ranked the procedures as follows: optimal, maximum likelihood, full multinomial and nearest neighbour rule.

Keywords: Binary Data, Classification Rules, Full Multinomial, Nearest Neighbour, Optimal, maximum likelihood

Inference for Generalized Exponential Distribution based on Generalized Order Statistics (Review Completed - Accepted)

Estimation of parameter of generalized exponential distribution (gexp) is obtained

based on generalized order statistics. The maximum likelihood and Bayes methods are

used for this purpose. Survival function and hazard rate are also computed. Estimation

based on upper record values from generalized exponential distribution is obtained as a

special case and compared by simulated data.

Keywords: Bayes estimation, Generalized exponential distribution, Survival function, hazard rate function., maximum likelihood, record values