Detecting atypical data in air pollution studies by using shorth intervals for regression

Cécile Durot; Karelle Thiébot

ESAIM: Probability and Statistics (2005)

  • Volume: 9, page 230-240
  • ISSN: 1292-8100

Abstract

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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.

How to cite

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Durot, Cécile, and Thiébot, Karelle. "Detecting atypical data in air pollution studies by using shorth intervals for regression." ESAIM: Probability and Statistics 9 (2005): 230-240. <http://eudml.org/doc/244864>.

@article{Durot2005,
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; Air pollution},
language = {eng},
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/244864},
volume = {9},
year = {2005},
}

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
PY - 2005
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; Air pollution
UR - http://eudml.org/doc/244864
ER -

References

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  1. [1] 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. 
  2. [2] C. Durot and K. Thiébot. Bootstrapping the shorth for regression. Submitted (2003). Zbl1187.62034
  3. [3] P. Hall, J.W. Kay and D.M. Titterington, Asymptotically optimal difference-based estimation of variance in nonparametric regression. Biometrika 77 (1990) 521–529. 
  4. [4] 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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