Log-Ratio Propagation on the Simplex: A Theory of Cellwise Contamination for Compositional Data
arXiv:2605.31345v1 Announce Type: cross Abstract: Compositional data must be analysed through log-ratios: scale invariance, the defining axiom of the field, leaves no alternative. The centred log-ratio divides by the geometric mean of every part, so a single contaminated component shifts every centred-log-ratio coordinate at once, displacing the log-ratio vector by a fixed amount that no choice of coordinates can reduce. We develop a theory of cellwise contamination on the simplex around this observation. A scale-invariant contamination model built from multiplicative perturbation combines wit
This academic paper was recently published on arXiv, representing a routine output of ongoing research in statistics and machine learning.
This highly technical paper on statistical methods for compositional data is of interest primarily to specialized researchers in the field, rather than a strategic reader.
The publication provides a new theoretical framework for handling contamination in compositional data, refining existing statistical methodologies but not altering broad market or geopolitical dynamics.
Refined statistical methodologies for compositional data analysis may emerge within academic research.
Improved robustness in machine learning models applied to certain types of statistical datasets, such as those in genomics or ecology, could be a long-term outcome.
Eventual, but distant, enhancements in data-driven decision making in highly specific analytical domains could materialize.
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