KERNEL CLASSIFICATION RULES FROM MISSING DATA

Nonparametric kernel classification rules derived from incomplete (missing) data are studied. A number of techniques of handling missing observations in the training set are taken into account. In particular, the straight forward approach of designing a classifier only from available data (deleting missing values) is considered. The class of imputation techniques is also taken into consideration. In the latter case, one estimates missing values and then calculates classification rules from such a completed training set. Consistency and speed of convergence of proposed classification rules are established. Results of simulation studies are also presented.