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[DBS02] Missing Data in Hierarchical Classification of Variables, a simulation study
Conférence Internationale avec comité de lecture : IFCS 2002, January 2002,
motcle:
Résumé:
Here we develop from a first work the effect of missing data in hierarchical classification of variables according to the following factors: amount of missing data, imputation techniques, similarity coefficient, and aggregation criterion. We have used two methods of imputation, a regression method using an OLS method and an EM algorithm. For the similarity matrices we have used the basic affinity coefficient and the Pearson's correlation coefficient. As aggregation criteria we apply average linkage, single linkage and complete linkage methods. To compare the structure of the hierarchical classifications the Spearman's coefficient between the associated ultrametrics has been used. We present here simulation experiments in five multivariate normal cases.
Commentaires:
8 eme Conférence de l'International Federation of Classification Societies, 16-19 juillet 2002, Cracovie
BibTeX
| @inproceedings { | |||
| DBS02, | |||
| title | = | "{Missing Data in Hierarchical Classification of Variables, a simulation study}", | |
| author | = | " A. Lorga da Silva and H. Bacelar-Nicolau and G. Saporta ", | |
| booktitle | = | "{IFCS 2002}", | |
| year | = | 2002, | |
| month | = | "January", | |
| note | = | "{8 eme Conférence de l'International Federation of Classification Societies, 16-19 juillet 2002, Cracovie}", | |
| } | |||
