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                                       Details van artikel 7 van 21 gevonden artikelen
 
 
  Comparison of algorithms for replacing missing data in discriminant analysis
 
 
Titel: Comparison of algorithms for replacing missing data in discriminant analysis
Auteur: Daniel, J.Twedt
Gill, D.S.
Verschenen in: Communications in statistics
Paginering: Jaargang 21 (1992) nr. 6 pagina's 1567-1578
Jaar: 1992
Inhoud: We examined the impact of different methods for replacing missing data in discriminant analyses conducted on randomly generated samples from multivariate normal and non-normal distributions. The probabilities of correct classification were obtained for these discriminant analyses before and after randomly deleting data as well as after deleted data were replaced using: (1) variable means, (2) principal component projections, and (3) the EM algorithm. Populations compared were: (1) multivariate normal with covariance matrices ∑1=∑2, (2) multivariate normal with ∑1≠∑2 and (3) multivariate non-normal with ∑1=∑2. Differences in the probabilities of correct classification were most evident for populations with small Mahalanobis distances or high proportions of missing data. The three replacement methods performed similarly but all were better than non - replacement.
Uitgever: Taylor & Francis
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

                             Details van artikel 7 van 21 gevonden artikelen
 
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