An outlier-resistant test for heteroscedasticity in linear models

The presence of contamination often called outlier is a very common attribute in data. Among other causes, outliers in a homoscedastic model make the model heteroscedastic. Moreover, outliers distort diagnostic tools for heteroscedasticity such that it may not be correctly identified. In this article, we show how outliers affect heteroscedasticity diagnostics. We then proposed a robust procedure for detecting heteroscedasticity in the presence of outliers by robustifying the non-robust component of the Goldfeld–Quandt (GQ) test. The performance of the proposed procedure is examined using simulation experiment and real data sets. The proposed procedure offers great improvement where the conventional GQ and other procedures fail.