MISSING-VALUES ADJUSTMENT FOR MIXED-TYPE DATA

In this paper we propose a new method of single imputation, reconstruction, and estimation of non-reported, incorrect or excluded values both in the target and in the auxiliary variables where the first is on ratio or interval scale and the last are heterogeneous in measurement scale. Our technique is a variation of the popular nearest neighbor hot deck imputation (NNHDI) where "nearest" is defined in terms of a global distance obtained as a convex combination of the partial distance matrices computed for the various types of variables. In particular, we address the problem of proper weighting the partial distance matrices in order to reflect their significance, reliability and statistical adequacy. Performance of several weighting schemes is compared under a variety of settings in coordination with imputation of the least power mean. We have demonstrated, through analysis of simulated and actual data sets, the appropriateness of this approach. Our main contribution has been to show that mixed data may optimally be combined to allow accurate reconstruction of missing values in the target variable even in the absence of some data in the other fields of the record.