Estimation of Discriminant Functions from a Mixture of Two Weibull Population Mean Distributions | ||||
| Assiut University Journal of Multidisciplinary Scientific Research | ||||
| Volume 54, Issue 3, September 2025, Page 404-415 PDF (519.6 K) | ||||
| Document Type: Novel Research Articles | ||||
| DOI: 10.21608/aunj.2025.355643.1115 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Omar Mahmoud Ahmed  1; shereen Ali Mohamed2; Ayman Mohamed Abd-Elrahman2
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| 1Mathematics, Faculty of science, Assiut university, Assiut, Egypt. | ||||
| 2Faculty of Science, Assiut University | ||||
| 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. | ||||
| Keywords | ||||
| finite mixtures; maximum-likelihood estimation; discriminant function; bias; mean-square error | ||||
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