, Volume 70, Issue 1, pp 59–77 | Cite as

Heteroscedasticity diagnostics for t linear regression models

  • Jin-Guan LinEmail author
  • Li-Xing Zhu
  • Feng-Chang Xie


The t regression models provide a useful extension of the normal regression models for datasets involving errors with longer-than-normal tails. Homogeneity of variances (if they exist) is a standard assumption in t regression models. However, this assumption is not necessarily appropriate. This paper is devoted to tests for heteroscedasticity in general t linear regression models. The asymptotic properties, including asymptotic Chi-square and approximate powers under local alternatives of the score tests, are studied. Based on the modified profile likelihood (Cox and Reid in J R Stat Soc Ser B 49(1):1–39, 1987), an adjusted score test for heteroscedasticity is developed. The properties of the score test and its adjustment are investigated through Monte Carlo simulations. The test methods are illustrated with land rent data (Weisberg in Applied linear regression. Wiley, New York, 1985).


Adjusted score test Approximate local powers Asymptotic properties Heteroscedasticity Score test t Regression models Simulation studies 


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Department of MathematicsSoutheast UniversityNanjingChina
  2. 2.Department of MathematicsHong Kong Baptist UniversityJiulongChina
  3. 3.Department of MathematicsNanjing Agricultural UniversityNanjingChina

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