Dimension test approach of heteroscedasticity in the linear model

Keunbaik Lee, Hyejin Song, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review


Heteroscedasticity testing has a long history and is still an important matter in the linear model. There exist many types of tests, but they are limited in use to their own specific cases and sensitive to normality. Here, we propose a dimension test approach to heteroscedasticity. The proposed test overcomes the shortcomings of the existing methods, so that it is robust to normality and is unified in sense that it is applicable in the linear model with multi-dimensional response. Numerical studies confirm that the proposed test is favorable over the existing tests with moderate sample sizes, and real data analysis is presented.

Original languageEnglish
Pages (from-to)4356-4366
Number of pages11
JournalCommunications in Statistics: Simulation and Computation
Issue number6
StatePublished - 3 Jul 2017

Bibliographical note

Publisher Copyright:
© 2017 Taylor & Francis Group, LLC.


  • Heteroscedasticity
  • Linear model
  • Permutation test
  • Sliced average variance estimation


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