Department of Mathematics
Heteroscedasticity diagnostics for t linear regression models
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). © 2008 Springer-Verlag.
Adjusted score test, Approximate local powers, Asymptotic properties, Heteroscedasticity, Score test, Simulation studies, T Regression models
Source Publication Title
Link to Publisher's Edition
Lin, J., Zhu, L., & Xie, F. (2009). Heteroscedasticity diagnostics for t linear regression models. Metrika, 70 (1), 59-77. https://doi.org/10.1007/s00184-008-0179-2