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

Cécile DurotKarelle Thiébot — 2005

ESAIM: Probability and Statistics

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...

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

Cécile DurotKarelle Thiébot — 2010

ESAIM: Probability and Statistics

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...

Bootstrapping the shorth for regression

Cécile DurotKarelle Thiébot — 2006

ESAIM: Probability and Statistics

The paper is concerned with the asymptotic distributions of estimators for the length and the centre of the so-called -shorth interval in a nonparametric regression framework. It is shown that the estimator of the length converges at the -rate to a Gaussian law and that the estimator of the centre converges at the -rate to the location of the maximum of a Brownian motion with parabolic drift. Bootstrap procedures are proposed and shown to be consistent. They...

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