Updating and asymptotic relative efficiency of a non-linear discriminant function estimated from a mixture of two Gompertz populations

Autor:	H. M. Moustafa, S. G. Ramadan
Rok vydání:	2004
Předmět:	Computational Mathematics Nonlinear system Efficiency Discriminant function analysis Applied Mathematics Non linear functions Statistics Gompertz function Probability distribution Word error rate Mathematics
Zdroj:	Applied Mathematics and Computation. 155:205-219
ISSN:	0096-3003
DOI:	10.1016/s0096-3003(03)00771-9
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::7d367761f8dd9878e010435811956cb3 https://doi.org/10.1016/s0096-3003(03)00771-9 Zobrazit plný text záznamu Full Text from ScienceDirect