RT info:eu-repo/semantics/article
T1 M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality
A1 Ranalli, M. Giovanna
A1 Salvati, Nicola
A1 Petrella, Lea
A1 Pantalone, Francesco
K1 Meteorología
K1 Contaminación
K1 Additive models
K1 B-splines
K1 Cross-validation
K1 Influence function
K1 Robust regression
K1 2509 Meteorología
K1 2509.02 Contaminación Atmosférica
AB In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) toidentify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a Bsplineon the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PMconcentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand,model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional response distribution
PB Wiley
SN 0323-3847
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/67428
UL https://uvadoc.uva.es/handle/10324/67428
LA spa
NO Biometrical Journal, Septiembre, 2023
NO Producción Científica
DS UVaDOC
RD 22-dic-2024