Estimation of Discriminant Functions from a Mixture of Two Weibull Population Mean Distributions
Year: 2025
Type: article
Abstract: In this paper, we present the Weibull Population Mean Distribution (WPMD), a procedure for maximum likelihood estimates (WPMD) of the parameters of a finite mixture of two Weibull Population Mean Distribution (WPMD) is presented, by classified and unclassified observations. Estimation of a nonlinear discriminant function on the basis of a different sample size is measured. Its presentation is examined by a series of simulation experiments. The simulations lead for estimating a nonlinear discriminant function by the maximum likelihood method, on the basis of unclassified data drawn from a mixture of the underlying populations propose that the error rate can be reduced by a substantial percentage for widely separated populations. In general, the presentation of the mixture discrimination procedure relative to the completely classified procedure, measured by total probabilities is good. In general, the presentation of the mixture discrimination procedure relative to the completely classified procedure, measured by total probabilities is good.
Authors: Omar Mahmoud Ahmed, shereen Ali Mohamed, Ayman Mohamed Abd-Elrahman
Institution: Assiut University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414045251