DEALING WITH ZEROS AND MISSING VALUES IN COMPOSITIONAL DATA SETS USING NONPARAMETRIC IMPUTATION

Abstract

The statistical analysis of compositional data based on logratios of parts is not suitable when zeros are present in a data set. Nevertheless, if there is interest in using this modeling approach, several strategies have been published in the specialized literature which can be used. In particular, substitution or imputation strategies are available for rounded zeros. In this paper, existing nonparametric imputation methods-both for the additive and the multiplicative approach-are revised and essential properties of the last method are given. For missing values a generalization of the multiplicative approach is proposed.