An outlier-resistant test for heteroscedasticity in linear models

Abstract

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.

Keywords:

• contamination
• Gaussian paradigm
• heteroscedasticity
• robust Goldfeld–Quandt test
• robustifying

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ekele Alih http://orcid.org/0000-0001-6840-5635

Hong Choon Ong http://orcid.org/0000-0002-3253-0538

Additional information

Funding

This work was supported in part by U.S.M. Fundamental Research Grant Scheme (FRGS) No. 203/PMATHS/6711319.