An analysis is bpresented based upon the Chebyshev approximation for the boundary layer flow of a viscoelastic fluid over a porous plate

An analysis is bpresented based upon the Chebyshev approximation for the boundary layer flow of a viscoelastic fluid over a porous plate

A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two random walk distributions in two cases, using classified and unclassified observations. Based on small sample size, estimation of nonlinear discriminant functions is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant functions is investigated. The total probabilities of misclassification and percentage biases are evaluated and discussed.

A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two random walk distributions in two cases, using classified and unclassified observations. Based on small sample size, estimation of nonlinear discriminant functions is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant functions is investigated. The total probabilities of misclassification and percentage biases are evaluated and discussed.

A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two random walk distributions in two cases, using classified and unclassified observations. Based on small sample size, estimation of nonlinear discriminant functions is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant functions is investigated. The total probabilities of misclassification and percentage biases are evaluated and discussed.

Classification problems associated with univariate Gompertz populations are studied. The robustness of the linear discriminant function, the normal classificatory rule, LDF when the underlying populations are Gompertz, is investigated. The errors of misclassification corresponding to LDF are compared with that due to the likelihood ratio LR rule for Gompertz populations. The asymptotic probability distributions for the actual error rates are derived, for large sample sizes. Theoretical and experimental comparisons are performed.

Classification problems associated with univariate Gompertz populations are studied. The robustness of the linear discriminant function, the normal classificatory rule, LDF when the underlying populations are Gompertz, is investigated. The errors of misclassification corresponding to LDF are compared with that due to the likelihood ratio LR rule for Gompertz populations. The asymptotic probability distributions for the actual error rates are derived, for large sample sizes. Theoretical and experimental comparisons are performed.

Updating a non-linear discriminant function estimated from Gompertz populations is investigated. The updating procedure is considered when the additional observations are mixed or classified. Using simulation experiments the performance of the updating procedures is evaluated via relative efficiencies. On the other hand, the asymptotic expectations of the total probabilities of misclassification for mixture and classified discrimination procedures are evaluated. Then the asymptotic efficiency of the mixture discrimination procedures relative to the completely classified are obtained and discussed for some combinations of the parameters.

Updating a non-linear discriminant function estimated from Gompertz populations is investigated. The updating procedure is considered when the additional observations are mixed or classified. Using simulation experiments the performance of the updating procedures is evaluated via relative efficiencies. On the other hand, the asymptotic expectations of the total probabilities of misclassification for mixture and classified discrimination procedures are evaluated. Then the asymptotic efficiency of the mixture discrimination procedures relative to the completely classified are obtained and discussed for some combinations of the parameters.

The property of identifiability is an important consideration on estimating the parameters in a mixture of distributions. Also classification of a random variable based on a mixture can be meaning fully discussed only if the class of all finite mixtures is identifiable. The problem of identifiability of finite mixture of Gompertz distributions is studied. A procedure is presented for finding maximum likelihood estimates of the parameters of a mixture of two Gompertz distributions, using classified and unclassified observations. Based on small sample size, estimation of a nonlinear discriminant function is considered. Throughout simulation experiments, the performance of the corresponding estimated nonlinear discriminant function is investigated.