Abstract
The log-ratio methodology represents a powerful set of methods and techniques to apply in the statistical
analysis of compositional data (CODA). These techniques are used for the estimation of rounded zeros
or values below the detection limit, in case that the underlying data are of compositional nature. An
algorithm based on iterative log-ratio regressions is developed, combining a particular family of isometric
log-ratio transformations with censored regression. We proof that classical and robust regression methods
are equivalent in this context when they are based on additive or isometric log-ratio transformations. Based
on Monte Carlo experiments, simulations are performed to assess the performance of classical and robust
methods. To illustrate the introduced method, a real study involving geochemical data is carried out.
Year
2012
Category
Refereed journal