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RobustF-tests for linear models

Taplin, R.H. (1999) RobustF-tests for linear models. Canadian Journal of Statistics, 27 (2). pp. 361-371.

Link to Published Version: http://dx.doi.org/10.2307/3315645
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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.

Item Type: Journal Article
Murdoch Affiliation: School of Mathematical and Physical Sciences
Publisher: Wiley-Blackwell
Copyright: © 2009 Statistical Society of Canada
URI: http://researchrepository.murdoch.edu.au/id/eprint/36224
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