[Back]

M. Templ, K. Hron, P. Filzmoser, A. Gardlo:
"Imputation of rounded zeros for high-dimensional compositional data";
Chemometrics and Intelligent Laboratory Systems, 155 (2016), 183 - 190.

High-dimensional compositional data, multivariate observations carrying rel-
ative information, frequently contain values below a detection limit (rounded zeros). We introduce new model-based procedures for replacing these val-
ues by reasonable numbers, so that the completed data set is ready for use with statistical analysis methods that rely on complete data, such as re-
gression or classification with high-dimensional explanatory variables. The procedures respect the geometry of compositional data and can be consid-
ered as alternatives to existing methods. Simulations show that especially in high-dimensions, the proposed methods outperform existing methods. More-
over, even for a large number of rounded zeros, the new methods lead to an improved quality of the data, which is important for further analyses.
The usefulness of the procedure is demonstrated using a data example from metabolomics.

high-dim. compositional data, rounded zeros, imputation

http://dx.doi.org/10.1016/j.chemolab.2016.04.011

http://www.sciencedirect.com/science/article/pii/S0169743916300958