Detecting atypical data in air pollution studies by using shorth intervals for regression
ESAIM: Probability and Statistics (2010)
- Volume: 9, page 230-240
- ISSN: 1292-8100
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topDurot, Cécile, and Thiébot, Karelle. "Detecting atypical data in air pollution studies by using shorth intervals for regression." ESAIM: Probability and Statistics 9 (2010): 230-240. <http://eudml.org/doc/104334>.
@article{Durot2010,
abstract = {
To validate pollution data, subject-matter experts in Airpl (an organization
that maintains a network of air pollution monitoring stations in western
France) daily perform visual examinations of the data and check their
consistency. In this paper, we describe these visual examinations and
propose a formalization for this problem. The examinations consist
in comparisons of so-called shorth intervals so we build a statistical
test that compares such intervals in a nonparametric regression model.
This allows to detect atypical data. A practical application
of the test is given.
},
author = {Durot, Cécile, Thiébot, Karelle},
journal = {ESAIM: Probability and Statistics},
keywords = {Air pollution; validation;
regression; bootstrap; shorth.; regression; shorth},
language = {eng},
month = {3},
pages = {230-240},
publisher = {EDP Sciences},
title = {Detecting atypical data in air pollution studies by using shorth intervals for regression},
url = {http://eudml.org/doc/104334},
volume = {9},
year = {2010},
}
TY - JOUR
AU - Durot, Cécile
AU - Thiébot, Karelle
TI - Detecting atypical data in air pollution studies by using shorth intervals for regression
JO - ESAIM: Probability and Statistics
DA - 2010/3//
PB - EDP Sciences
VL - 9
SP - 230
EP - 240
AB -
To validate pollution data, subject-matter experts in Airpl (an organization
that maintains a network of air pollution monitoring stations in western
France) daily perform visual examinations of the data and check their
consistency. In this paper, we describe these visual examinations and
propose a formalization for this problem. The examinations consist
in comparisons of so-called shorth intervals so we build a statistical
test that compares such intervals in a nonparametric regression model.
This allows to detect atypical data. A practical application
of the test is given.
LA - eng
KW - Air pollution; validation;
regression; bootstrap; shorth.; regression; shorth
UR - http://eudml.org/doc/104334
ER -
References
top- L. Bel, L. Bellanger, V. Bonneau, G. Ciuperca, D. Dacunha-Castelle, C. Deniau, B. Ghattas, Y. Misiti and G. Oppenheim, Éléments de comparaison de prévisions statistiques des pics d'ozone. Rev. Statist. App.3 (1999) 7–25.
- C. Durot and K. Thiébot. Bootstrapping the shorth for regression. Submitted (2003). Zbl1187.62034
- P. Hall, J.W. Kay and D.M. Titterington, Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika77 (1990) 521–529.
- K. Thiébot, Synthèse de l'enquête sur la procédure de validation de données dans les résaux de surveillance de pollution athmosphérique. Technical report, Air Pays de la Loire (1998).
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