Journal article
RobustF-tests for linear models
Canadian Journal of Statistics, Vol.27(2), pp.361-371
1999
Abstract
The author presents a robust F-test for comparing nested linear models. It is suggested that the approach will be attractive to practitioners because it is based on the familiar F-statistic and corresponds to the common practice of reporting F-statistics after removing obvious outliers. It is calibrated in terms of a real parameter that can be directly interpreted as the willingness of the data analyst to remove observations, and the sensitivity of the F-statistic to this parameter is easily examined. The procedure is evaluated with a simulation study where a scale mixture distribution is used to generate outliers. The procedure is also applied to some data where the occurrence of an outlier is confounded with the significance of a regression term. This provides a comparison of two competing models for the data: one removing an outlier and the other including an additional regression term instead.
Details
- Title
- RobustF-tests for linear models
- Authors/Creators
- R.H. Taplin (Author/Creator) - Murdoch University
- Publication Details
- Canadian Journal of Statistics, Vol.27(2), pp.361-371
- Publisher
- Wiley-Blackwell
- Identifiers
- 991005544653707891
- Copyright
- © 2009 Statistical Society of Canada
- Murdoch Affiliation
- School of Mathematical and Physical Sciences
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 9 Mathematics
- 9.92 Statistical Methods
- 9.92.220 Robust Estimation
- Web Of Science research areas
- Statistics & Probability
- ESI research areas
- Mathematics