Article Type:
Research Article

Application and Comparison of Robust Linear Regression Methods for Trend Estimation

Andreas Muhlbauer Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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Peter Spichtinger Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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Ulrike Lohmann Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland

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Print Publication:
01 Sep 2009
DOI:
https://doi.org/10.1175/2009JAMC1851.1
Page(s):
1961–1970
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Abstract

In this study, robust parametric regression methods are applied to temperature and precipitation time series in Switzerland and the trend results are compared with trends from classical least squares (LS) regression and nonparametric approaches. It is found that in individual time series statistically outlying observations are present that influence the LS trend estimate severely. In some cases, these outlying observations lead to an over-/underestimation of the trends or even to a trend masking. In comparison with the classical LS method and standard nonparametric techniques, the use of robust methods yields more reliable trend estimations and outlier detection.

* Current affiliation: Joint Institute for the Study of the Atmosphere and Ocean/Department of Atmospheric Sciences, University of Washington, Seattle, Washington.

Corresponding author address: Andreas Muhlbauer, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland. Email: andreasm@atmos.washington.edu

Abstract

In this study, robust parametric regression methods are applied to temperature and precipitation time series in Switzerland and the trend results are compared with trends from classical least squares (LS) regression and nonparametric approaches. It is found that in individual time series statistically outlying observations are present that influence the LS trend estimate severely. In some cases, these outlying observations lead to an over-/underestimation of the trends or even to a trend masking. In comparison with the classical LS method and standard nonparametric techniques, the use of robust methods yields more reliable trend estimations and outlier detection.

* Current affiliation: Joint Institute for the Study of the Atmosphere and Ocean/Department of Atmospheric Sciences, University of Washington, Seattle, Washington.

Corresponding author address: Andreas Muhlbauer, Institute for Atmospheric and Climate Science, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland. Email: andreasm@atmos.washington.edu

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