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Bartlett corrections in heteroskedastic t regression models

Author

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  • Barroso, Lúcia P.
  • Cordeiro, Gauss M.

Abstract

This paper gives a general Bartlett correction formula to improve likelihood ratio tests in heteroskedastic t regression models where both location and dispersion parameters vary with the observations. The correction covers many important and commonly used models and can be viewed as an extension of the results in Cordeiro [1993. Bartlett corrections and bias correction for two heteroscedastic regression models, Comm. Statist. Theory Methods 22, 169-188] and Botter and Cordeiro [1997. Bartlett corrections for generalized linear models with dispersion covariates, Comm. Statist. Theory Methods 26, 279-307]. We present some Monte Carlo investigations of Bartlett corrections that show that this approach has better performance than the classical likelihood ratio tests even under degrees of freedom misspecification.

Suggested Citation

  • Barroso, Lúcia P. & Cordeiro, Gauss M., 2005. "Bartlett corrections in heteroskedastic t regression models," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 86-96, November.
    • Handle: RePEc:eee:stapro:v:75:y:2005:i:2:p:86-96
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    Citations

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    Cited by:

    1. Jin-Guan Lin & Li-Xing Zhu & Feng-Chang Xie, 2009. "Heteroscedasticity diagnostics for t linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(1), pages 59-77, June.
    2. Jin-Guan Lin & Li-Xing Zhu & Chun-Zheng Cao & Yong Li, 2011. "Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1509-1531, August.
    3. Francisco M. C. Medeiros & Silvia L. P. Ferrari, 2017. "Small-sample testing inference in symmetric and log-symmetric linear regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(3), pages 200-224, August.

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