Principal Component Analysis with Interval Imputed missing values

In this paper some statistical properties of Interval Imputation are derived
in the context of Principal Component Analysis. Interval Imputation is a recent proposal
for the treatment of missing values, consisting of replacing blanks with intervals
and then analyzing the resulting data matrix using Symbolic Data Analysis
techniques. The most noticeable virtue of this method is that it does not require a
single-valued imputation, so it allows us to take into account that incomplete observations
are affected by a degree of uncertainty. Illustrative examples and simulation
studies are carried out in order to illustrate the functioning of the technique.