A variance-based, BIC-penalized variable selection method identifies influential interferents in sensor calibration and improves pollutant prediction in simulated and outdoor carbon nanotube sensor settings.
Optimizing sensor calibra- 27 tion in open environments: A bayesian approach for non-specific multisensory systems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
stat.AP 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Variance-based variable selection in sensor calibration with strong interferents -- application to air pollution monitoring with a carbon nanotube sensor array
A variance-based, BIC-penalized variable selection method identifies influential interferents in sensor calibration and improves pollutant prediction in simulated and outdoor carbon nanotube sensor settings.