Abstract
This paper briefly describes statistical methods for analyzing imprecise compositional data that might be elicited from approximate measurement or from expert judgments. Two alternative approaches are discussed: Log-ratio transforms and probability-ratio transforms. The first is well-established and the second is under development by the author. The primary focus in this paper is on generalized linear models for predicting imprecise compositional data.
| Original language | English |
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| Pages (from-to) | 364-366 |
| Number of pages | 3 |
| Journal | Proceedings of Machine Learning Research |
| Volume | 103 |
| Publication status | Published - 2019 |
| Event | 11th International Symposium on Imprecise Probabilities: Theories and Applications, ISIPTA 2019 - Ghent, Belgium Duration: 3 Jul 2019 → 6 Jul 2019 |