Two algorithms for estimation of number of classes are presented. Multiple extrema problem is also concerned. Use of marginal distributions when data are incomplete still lacks theoretical validation. Necessary theorems are proven. The relevant distributions under autoregression model are concerned for incomplete data.
Optimal rule is given for classification with missing data in the parametric case.
Some semi-parametric models also perform well in tests. Estimates are easy to EM-train. A method is proposed that does not induce these effects. A nonparametric test for the two-way variance analysis based on multivariate symmetry properties is proposed. Unlike the Friedman test the proposed test is consistent against all alternatives.