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Robust tests for heteroscedasticity using outlier detection methods
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 Title & Authors
Robust tests for heteroscedasticity using outlier detection methods
Seo, Han Son; Yoon, Min;
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There is a need to detect heteroscedasticity in a regression analysis; however, it invalidates the standard inference procedure. The diagnostics on heteroscedasticity may be distorted when both outliers and heteroscedasticity exist. Available heteroscedasticity detection methods in the presence of outliers usually use robust estimators or separating outliers from the data. Several approaches have been suggested to identify outliers in the heteroscedasticity problem. In this article conventional tests on heteroscedasticity are modified by using a sequential outlier detection methods to separate outliers from contaminated data. The performance of the proposed method is compared with original tests by a Monte Carlo study and examples.
heteroscedasticity;linear regression model;outliers;robust tests;
 Cited by
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