The assessment of the ecological risk of chemical contamination by pollutants, pesticides or toxicants is of primary interest in environmental statistics. Concentration-response models play a fundamental role in computing the risk values connected with some exposure levels of a particular contaminant in living organisms. The present paper proposes a regression model called simplicial regression. This model is able to cope with the relative character of the explanatory and response parts via the logratio methodology of compositional data. Consequently, it allows performance of the corresponding statistical inference under the assumption of normality. Some real-world examples show that simplicial regression even outperforms the existing well-established methodologies on standard accuracy and quality-of-fit criteria. The better fit is due to the change of scale entailed by the new model.
Monti, G., Migliorati, S., Hron, K., Hrůzová, K., Fišerová, E. (2015). Log-ratio approach in curve fitting for concentration-response experiments. ENVIRONMENTAL AND ECOLOGICAL STATISTICS, 22(2), 275-295 [10.1007/s10651-014-0298-z].
Log-ratio approach in curve fitting for concentration-response experiments
MONTI, GIANNA SERAFINA
;MIGLIORATI, SONIASecondo
;
2015
Abstract
The assessment of the ecological risk of chemical contamination by pollutants, pesticides or toxicants is of primary interest in environmental statistics. Concentration-response models play a fundamental role in computing the risk values connected with some exposure levels of a particular contaminant in living organisms. The present paper proposes a regression model called simplicial regression. This model is able to cope with the relative character of the explanatory and response parts via the logratio methodology of compositional data. Consequently, it allows performance of the corresponding statistical inference under the assumption of normality. Some real-world examples show that simplicial regression even outperforms the existing well-established methodologies on standard accuracy and quality-of-fit criteria. The better fit is due to the change of scale entailed by the new model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.