Log-ratio approach in curve fitting for concentration-response experiments

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 contaminan...

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Bibliographic Details
Published in:Environmental and ecological statistics 2015-06, Vol.22 (2), p.275-295
Main Authors: Monti, Gianna S., Migliorati, Sonia, Hron, Karel, Hrůzová, Klára, Fišerová, Eva
Format: Article
Language:English
Subjects:
Binomial distribution
Biomedical and Life Sciences
Chemical contaminants
Chemical contamination
Chemical pollution
Chemistry and Earth Sciences
Computer Science
Contaminants
Ecology
Environmental statistics
Generalized linear models
Health Sciences
Life Sciences
Math. Appl. in Environmental Science
Medicine
Pesticide toxicity
Pesticides
Physics
Pollution
Regression analysis
Risk assessment
Statistics
Statistics for Engineering
Statistics for Life Sciences
Studies
Theoretical Ecology/Statistics
Toxicants
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Summary: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.
ISSN:1352-8505
1573-3009
DOI:10.1007/s10651-014-0298-z