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Methods Inf Med 2004; 43(05): 510-515
DOI: 10.1055/s-0038-1633908
Original Article
Schattauer GmbH

Annoyance from Multiple Transportation Noise: Statistical Models and Outlier Detection

S. Kuhnt
1   Department of Statistics, University of Dortmund, Dortmund, Germany
,
C. Schürmann
1   Department of Statistics, University of Dortmund, Dortmund, Germany
,
B. Griefahn
2   Institute for Occupational Physiology at the University of Dortmund, Dortmund, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
05 February 2018 (online)

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Summary

Objective: Statistical models for the annoyance from multiple transportation noise are needed to understand and predict the annoyance resulting from specific noise exposures.

Methods: Models from the class of generalized linear models are suggested and discussed. Observations which are not well explained by the considered model are regarded as outliers. Outlier detection methods are applied to the data modelled by robust estimates using different link functions.

Results: The discussed methods are applied to data from a laboratory experiment using generalized linear models. While considering outliers, a generalized linear model with a complementary log-log link is found to be a good choice in modelling the exposure-response relationship between noise levels and annoyance.

Keywords

Generalized linear models - transportation noise - outliers - link function
 

  • References

  • 1 Berglund B, Lindvall T, Schwela DH. Guidelines for Community Noise. Geneva: WHO; 2000
    Google Scholar
  • 2 Miedema HME, Vos H. Exposure-response relationships for transportation noise. Journal of the Acoustical Society of America 1998; 104: 3432-45.
    CrossrefPubMedGoogle Scholar
  • 3 Finegold LS, Harris CS, von Gierke HE. Community annoyance and sleep disturbance: updated criteria for assessing the impacts of general transportation noise on people. Noise Control Engineering Journal 1994; 42: 25-30.
    CrossrefPubMedGoogle Scholar
  • 4 Hellbrück J. Category-Subdivision Scaling – A Powerful Tool in Audiometry and Noise Assessment. In Fastl H, Kuwano S, Schick A. eds Recent trends in research. Festschrift for Seiichiro Namba. Oldenburg: BIS; 1996: 317-336.
    Google Scholar
  • 5 McCullagh P, Nelder JA. Generalized Linear Models. London: Chapman & Hall; 1989
    Google Scholar
  • 6 Fahrmeir L, Tutz G. Multivariate Statistical Modelling Based on Generalized Linear Models. New York: Springer; 2001
    Google Scholar
  • 7 Davies L, Gather U. The identification of multiple outliers. Journal of the American Statististical Association 1993; 88: 782-92.
    PubMedGoogle Scholar
  • 8 Kuhnt S, Pawlitschko J. Outlier Identification Rules for Generalized Linear Models. To appear in. Baier D, Wernecke K.-D. (eds.) Innovations in Classification, Data Science, and Information Systems. Heidelberg: Springer; 2004
    Google Scholar
  • 9 Christmann A. Robust estimation in generalized linear models. In Kunert J, Trenkler G. eds Mathematical Statistics with Applications in Biometry. Festschrift in honour of Prof. Dr. Siegfried Siegfried Schach. Lohmar/Cologne: Josef Eul Verlag; 2000
    Google Scholar
  • 10 Rousseeuw PJ, Leroy AM. Robust Regression and Outlier Detection. New York: Wiley; 1987
    Google Scholar
  • 11 Davis S. Statistical Methods for the Analysis of Repeated Measurements. New York: Springer; 2002
    Google Scholar
  • 12 Agresti A. Categorical Data Analysis (Second Edition). New York: Wiley; 2002
    Google Scholar