The efficiency of a nonlinear discriminant function based on unclassified initial samples from a mixture of two Weibull populations
| Autor: | H. M. Moustafa |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Statistics and Probability
Applied Mathematics Mathematical analysis Expected value Linear discriminant analysis Nonlinear system Efficiency Discriminant Discriminant function analysis Modeling and Simulation Statistics Statistics Probability and Uncertainty Asymptotic expansion Weibull distribution Mathematics |
| Zdroj: | Journal of Statistical Computation and Simulation. 75:65-73 |
| ISSN: | 1563-5163 0094-9655 |
| DOI: | 10.1080/00949650410001660793 |
| Popis: | A nonlinear discriminant rule may be estimated by maximum likelihood estimation using unclassified observations. The performance of a nonlinear discriminant function based on a sample from a mixture of two Weibull distributions, with parameters λ, θ1, θ2 and p, is examined. Asymptotic expansion and asymptotic expected values of probabilities of misclassification are presented. The asymptotic relative efficiencies (AREs) of mixture and classified discrimination procedures are evaluated and discussed for selected parameters. Computations show that for fixed λ and p, as Δ = | θ1 − θ2| increases the ARE increases. Furthermore, for fixed λ and Δ, as p varies from 0.2 to 0.8 the values of ARE decrease. On the other hand, for fixed p and Δ, the ARE in case of λ = 0.5 are close to the ARE in the case of λ = 2. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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